• Annual Conference of KIPS
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• 2008.05a
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• pp.1035-1038
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• 2008

Network mobility (NEMO) has been studied extensively due to its potential applications in military and public transportation. NEMO Basic Support Protocol (NBSP) [1], the current NEMO standard based on mobile IPv6, can be readily deployed using the existing mobile IPv6 infrastructure. However, for Nested network mobility, multi-level tunnel and too many Binding Update packets results in substantial performance overhead, generally known as route sub-optimality, especially in the bottleneck root mobile router (root-MR) and Access Router. In this paper, we propose a route optimization mechanism for nested network mobility management to reduce the overhead of root-MR. In this system, Mobile Router (MR) has a cache that stores Mobile Network Nodes' (MNN) information, Correspondent Nodes' (CN) information for every MNN，and the attachments information with its subnet MRs. Home Agent performs Binding Update with CNs responsible for MRs. Through this mechanism, the number of tunnel is limited between CN and MR and the overhead of root-MR is reduced obviously.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.5
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• pp.18-24
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• 2013

Network coding has been extensively studied to increase the network throughput by exploiting the broadcast and overhearing capabilities. In this paper, we formulate a network utility maximization problem to improve the network utility in multichannel, multiradio and multisession wireless multihop network with intrasession network coding. To find the solution of this problem, we propose a congestion control, distributed rate control, and heuristic resource allocation algorithm. We find the network utility and evaluate the performance in multichannel, multiradio and multisession environment by using MATLAB. Finally, the results show that the proposed schemes can achieve throughput improvement by performing the network utility optimization in wireless multihop network.

• International Journal of Naval Architecture and Ocean Engineering
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• v.9 no.4
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• pp.373-381
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• 2017

A Collaborative Optimization (CO) methodology for ring-stiffened composite material pressure hull of underwater vehicle is proposed. Structural stability and material strength are both examined. Lamination parameters of laminated plates are introduced to improve the optimization efficiency. Approximation models are established based on the Ellipsoidal Basis Function (EBF) neural network to replace the finite element analysis in layout optimizers. On the basis of a two-level optimization, the simultaneous structure material collaborative optimization for the pressure vessel is implemented. The optimal configuration of metal liner and frames and composite material is obtained with the comprehensive consideration of structure and material performances. The weight of the composite pressure hull decreases by 30.3% after optimization and the validation is carried out. Collaborative optimization based on the lamination parameters can optimize the composite pressure hull effectively, as well as provide a solution for low efficiency and non-convergence of direct optimization with design variables.

• Journal of Korean Institute of Industrial Engineers
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• v.34 no.2
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• pp.181-189
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• 2008

Survivability of a network is one of the most important issues in designing present-day communication networks. For the past few decades, a lot of researches have proposed the mathematical models to evaluate the survivability of networks. In this paper, we attempt to survey such researches and classify them based on how these researches measure the survivability of a network.

• 전기의세계
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• v.26 no.1
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• pp.63-70
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• 1977

This paper deals with computer analysis technique of the network having 3-terminal elements whose input and output characteristics are defined by nonuniform spacing function group on the volt-ampere space. Developing the algorithms to obtain the solutions of the network mentioned above by computer, we propose optimization technique, which can solve the normal form equations of the network defined in this paper and which involves mode analysis technique to be able to analyze the case that the function group has negative resistance characteristics.

• Journal of the Korean Professional Engineers Association
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• v.42 no.6
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• pp.36-41
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• 2009

Smart Grid is grafting IT(information technology) techniques on existing electric power network, supplier and the consumer to do real-time exchange of information lead to both direction and energy efficiency optimization, it is a next potential electric power network method. Because of applying various distributed electric power sources, the electric power network system will follow in size and it is dispersive and it will operate independently, and it become the intelligent electric power network, which in consumer demand reacts at real-time, because of using various sensors. In this article explain concept, features, and contemporary background of Smart Grid, and describe improve reliability of the electric power quality.

• Journal of the Korean Data and Information Science Society
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• v.15 no.2
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• pp.331-337
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• 2004

In the analysis of massive volume data, a neural network model is a useful tool. To implement the Neural network model, it is important to select initial value. Since the initial values are generally used as random value in the neural network, the convergent performance and the prediction rate of model are not stable. To overcome the drawback a possible method use samples randomly selected from the whole data set. That is, coefficients estimated by logistic regression based on the samples are the initial values.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.39 no.5
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• pp.433-441
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• 2011

The optimization of a centrifugal compressor was conducted. The ANN (Artificial Neural Network) was adopted as an optimization algorithm, and it was learned and trained with the DOE (Design of Experiment). In the DOE, it was predicted the main effect and the interaction effect of design variables to the objective function. The ANN was improved in the optimization process using the GA (Genetic Algorithm). When any output at each generation was reached a standard level, it was re-calculated by the CFD (Computational Fluid Dynamics) and it was applied to develop a new ANN. After 6th generation, the prediction difference between ANN and CFD was less than 1%. A pareto of the efficiency versus the pressure ratio was obtained through the 21th generation. Using this method, the computational time for the optimization was equivalent to the time consumed by the gradient method, and the optimized results of multi-objective function were obtained.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2002.11a
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• pp.251-254
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• 2002

In the design of mobile wireless communication system, the location of BTS(Base Transciver Stations), RSC(Base Station Controllers), and MSC(Mobile Switching Center) is one of the most important parameters. Designing wireless communication system, the cost of equipment is need to be made low by combining various, complex parameters. We can solve this problem by combinatorial optimization algorithm, such as Simulated Annealing, Tabu Search, Genetic Algorithm, Random Walk Algorithm that have been extensively used for global optimization. This paper shows the four kind of algorithms which are applied to the location optimization of BTS, BSC, and MSC in designing mobile communication system and then we compare with these algorithms. And also we analyze the experimental results and shows the optimization process of these algorithms. As a the channel of a CDMA system is shared among several users, the receivers face the problem of multiple-access interference (MAI). Also, the multipath scenario leads to intersymbol interference (ISI). Both components are undesired, but unlike the additive noise process, which is usually completely unpredictable, their space-time structure helps to estimate and remove them.

• International Journal of Naval Architecture and Ocean Engineering
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• v.7 no.4
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• pp.750-769
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• 2015

The geometry of engineering systems affects their performances. For this reason, the shape of engineering systems needs to be optimized in the initial design stage. However, engineering system design problems consist of multi-objective optimization and the performance analysis using commercial code or numerical analysis is generally time-consuming. To solve these problems, many engineers perform the optimization using the approximation model (response surface). The Response Surface Method (RSM) is generally used to predict the system performance in engineering research field, but RSM presents some prediction errors for highly nonlinear systems. The major objective of this research is to establish an optimal design method for multi-objective problems and confirm its applicability. The proposed process is composed of three parts: definition of geometry, generation of response surface, and optimization process. To reduce the time for performance analysis and minimize the prediction errors, the approximation model is generated using the Backpropagation Artificial Neural Network (BPANN) which is considered as Neuro-Response Surface Method (NRSM). The optimization is done for the generated response surface by non-dominated sorting genetic algorithm-II (NSGA-II). Through case studies of marine system and ship structure (substructure of floating offshore wind turbine considering hydrodynamics performances and bulk carrier bottom stiffened panels considering structure performance), we have confirmed the applicability of the proposed method for multi-objective side constraint optimization problems.