• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.9B
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• pp.834-844
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• 2003

Mobile IP has some performance degrade factors such as triangle routing and packet loss by the hand-off because the original IP was designed for the fixed network. The design goal for the next generation Internet service is to guarantee QoS. So, Mobile IP also should be able to provide the guaranteed QoS with performance enhancement because it is an IP-based mobile Internet service. In this paper, we propose route optimization, smooth hand-off scheme and MIP-LDP(Mobile IP-Label Distribution Protocol) on Mobile IP-over-MPLS framework to enhance the performance of the previously researched Mobile IP-over-MPLS schemes. The proposed framework enhanced long routing path problem and packet loss problem by the hand-off.

• The KSFM Journal of Fluid Machinery
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• v.17 no.1
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• pp.35-40
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• 2014

A multi-objective optimization of a regenerative fan for enhancing the aerodynamic and aeroacoustic performance was carried out using an integrated fan design system, namely, Total FAN-Regen$^{(R)}$. The Total FAN-Regen$^{(R)}$ was developed for non-specialists to carry out a series of design process, viz., computational preliminary design, three-dimensional aerodynamic and aeroacoustic analyses, and design optimization, for a regenerative fan. An aerodynamic analysis of the regenerative fan was conducted by solving three-dimensional Reynolds-averaged Navier-Stokes equations using the shear stress transport turbulence model. And, an aeroacoustic analysis of the regenerative fan was implemented in a finite/infinite element method by solving the variational formulation of Lighthill's analogy based on the results of the unsteady flow analysis. An optimum shape obtained by Total FAN-Regen$^{(R)}$ shows the enhanced efficiency and decreased sound pressure level as much as 1.5 % and 20.0 dB, respectively, compared to those of the reference design. The performance test was carried out for an optimized regenerative fan to validate the performance of the numerically predicted optimal design.

• The Journal of the Korea Contents Association
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• v.13 no.9
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• pp.20-27
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• 2013

In recently years, with the growth of social media and the development of mobile devices, the data have been significantly increased. MapReduce is an emerging programming model that processes large amount of data. However, since MapReduce evenly places the data in the dedicated distributed computing environment, it is not suitable to the non-dedicated distributed computing environment. The data replacement algorithms were proposed for performance optimization of MapReduce in the non-dedicated distributed computing environments. However, they spend much time for date replacement and cause the network load for unnecessary data transmission. In this paper, we propose an efficient data replacement algorithm for the performance optimization of MapReduce in the non-dedicated distributed computing environments. The proposed scheme computes the ratio of data blocks in the nodes based on the node availability model and reduces the network load by transmitting the data blocks considering the data placement. Our experimental results show that the proposed scheme outperforms the existing scheme.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39A no.9
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• pp.519-527
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• 2014

Adaptive arrays can minimize contributions from interferences incident onto an sensor array while preserving a signal the direction vector of which corresponds to the array steering vector to within a scalar factor. If there exist errors in the steering vector, severe performance degradation can be caused since the desired signal is misunderstood as an interference by the array. This paper presents an adaptive beamforming method which is robust against steering vector errors, exploiting a range of the desired signal direction. In the presented method, an correlation matrix of array response vectors is obtained through integration over the direction range and a minimization problem is formulated using some eigenvectors of the correlation matrix such that a more accurate steering vector than initially given one can be found. The minimization problem is transformed into a relaxed SDP (semidefinite program) problem, which can be effectively solved since it is a sort of convex optimization. Simulation results show that the proposed method outperforms existing ones such as ORM (outside-range-based method) and USM (uncertainty-based method).

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.4
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• pp.862-870
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• 2011

In this paper, we introduce a new topology of Radial Basis Function-based Polynomial Neural Networks (RPNN) that is based on a genetically optimized multi-layer perceptron with Radial Polynomial Neurons (RPNs). This study offers a comprehensive design methodology involving mechanisms of optimization algorithms, especially Fuzzy C-Means (FCM) clustering method and Particle Swarm Optimization (PSO) algorithms. In contrast to the typical architectures encountered in Polynomial Neural Networks (PNNs), our main objective is to develop a design strategy of RPNNs as follows : (a) The architecture of the proposed network consists of Radial Polynomial Neurons (RPNs). In here, the RPN is fully reflective of the structure encountered in numeric data which are granulated with the aid of Fuzzy C-Means (FCM) clustering method. The RPN dwells on the concepts of a collection of radial basis function and the function-based nonlinear (polynomial) processing. (b) The PSO-based design procedure being applied at each layer of RPNN leads to the selection of preferred nodes of the network (RPNs) whose local characteristics (such as the number of input variables, a collection of the specific subset of input variables, the order of the polynomial, and the number of clusters as well as a fuzzification coefficient in the FCM clustering) can be easily adjusted. The performance of the RPNN is quantified through the experimentation where we use a number of modeling benchmarks - NOx emission process data of gas turbine power plant and learning machine data(Automobile Miles Per Gallon Data) already experimented with in fuzzy or neurofuzzy modeling. A comparative analysis reveals that the proposed RPNN exhibits higher accuracy and superb predictive capability in comparison to some previous models available in the literature.

• Advances in robotics research
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• v.2 no.2
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• pp.161-182
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• 2018

This paper proposes a novel receding horizon control (RHC) algorithm for formation control of a swarm of unmanned surface vehicles (USVs) using particle swarm optimization (PSO). The proposed control algorithm provides the coordinated path tracking of multi-agent USVs while preventing collisions and considering external disturbances such as ocean currents. A three degrees-of-freedom kinematic model of the USV is used for the RHC with guaranteed stability and convergence by incorporating a sequential Monte Carlo (SMC)-based particle initialization. An ocean current model-based estimator is designed to compensate for the effect of ocean currents on the USVs. This method is compared with the PSO-based RHC algorithms to demonstrate the performance of the formation control and the collision avoidance in the presence of ocean currents through numerical simulations.

• Journal of Communications and Networks
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• v.8 no.1
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• pp.116-122
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• 2006

Most of the multimedia applications require strict quality of service (QoS) guarantee during the communication between a single source and multiple destinations. This gives rise to the need for an efficient QoS multicast routing strategy. Determination of such QoS-based optimal multicast routes basically leads to a multi-objective optimization problem, which is computationally intractable in polynomial time due to the uncertainty of resources in Internet. This paper describes a network model for researching the routing problem and proposes a new multicast tree selection algorithm based on genetic algorithms to simultaneously optimize multiple QoS parameters. The paper mainly presents a QoS multicast routing algorithm based on genetic algorithm (QMRGA). The QMRGA can also optimize the network resources such as bandwidth and delay, and can converge to the optimal or near-optimal solution within few iterations, even for the networks environment with uncertain parameters. The incremental rate of computational cost can close to polynomial and is less than exponential rate. The performance measures of the QMRGA are evaluated using simulations. The simulation results show that this approach has fast convergence speed and high reliability. It can meet the real-time requirement in multimedia communication networks.

• International Journal of Naval Architecture and Ocean Engineering
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• v.2 no.4
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• pp.217-222
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• 2010

Since 1980's, optimum design techniques for ship structural design have been developed to the preliminary design which aims at minimum weight or minimum cost design of mid-ship section based on analytic structural analysis. But the optimum structural design researches about the application for the detail design of local structure based on FEA have been still insufficient. This paper presents optimization technique for the detail design of a racing motor boat. To improve the performance and reduce the damage of a real existing racing boat, direct structural analyses; static and non-linear transient dynamic analyses, were carried out to check the constraints of minimum weight design. As a result, it is shown that the optimum structural design of a racing boat has to be focused on reducing impulse response from pitching motion than static response because the dynamic effect is more dominant. Optimum design algorithm based on nonlinear finite element analysis for a racing motor boat was developed and coded to ANSYS, and its applicability for actual structural design was verifed.

• Journal of Electrical Engineering and Technology
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• v.8 no.3
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• pp.621-632
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• 2013

In this paper, we introduce advanced architectures of genetically-oriented Fuzzy Neural Networks (FNNs) based on fuzzy set and fuzzy relation and discuss a comprehensive design methodology. The proposed FNNs are based on 'if-then' rule-based networks with the extended structure of the premise and the consequence parts of the fuzzy rules. We consider two types of the FNNs topologies, called here FSNN and FRNN, depending upon the usage of inputs in the premise of fuzzy rules. Three different type of polynomials function (namely, constant, linear, and quadratic) are used to construct the consequence of the rules. In order to improve the accuracy of FNNs, the structure and the parameters are optimized by making use of genetic algorithms (GAs). We enhance the search capabilities of the GAs by introducing the dynamic variants of genetic optimization. It fully exploits the processing capabilities of the FNNs by supporting their structural and parametric optimization. To evaluate the performance of the proposed FNNs, we exploit a suite of several representative numerical examples and its experimental results are compared with those reported in the previous studies.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.1
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• pp.33-38
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• 2006

In this paper, we propose a new architecture of Fuzzy Polynomial Neural Networks(FPNN) by means of genetically optimized Fuzzy Polynomial Neuron(FPN) and discuss its comprehensive design methodology involving mechanisms of genetic optimization, especially Genetic Algorithms(GAs). The conventional FPNN developed so far are based on mechanisms of self-organization and evolutionary optimization. The design of the network exploits the extended Group Method of Data Handling(GMDH) with some essential parameters of the network being provided by the designer and kept fixed throughout the overall development process. This restriction may hamper a possibility of producing an optimal architecture of the model. The proposed FPNN gives rise to a structurally optimized network and comes with a substantial level of flexibility in comparison to the one we encounter in conventional FPNNs. It is shown that the proposed advanced genetic algorithms based Fuzzy Polynomial Neural Networks is more useful and effective than the existing models for nonlinear process. We experimented with Medical Imaging System(MIS) dataset to evaluate the performance of the proposed model.