• 경영과학
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• 제17권1호
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• pp.55-64
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• 2000

By managing not only geographical information but also various kinds of attribute data. GIS presents useful information for decision-makings. Most of decision-making problems using GIS can be formulated into network-optimization problems. In this study we deal with the implementation of network optimization system that extracts data from the database in GIS. solves a network optimization problem and present optimal solutions through GIS' graphical user interface. We design a nitwork optimization system and present some implementation techniques by showing a prototype of the network optimization system. Our network optimization system consists of three components : the interface module for user and GIS the basic network the program module the advanced network optimization program module. To handle large-scale networks the program module including various techniques for large sparse networks is also considered, For the implementation of the network optimization system we consider two approaches : the method using script languages supported by GIS and the method using client tools of GIS. Finally some execution results displayed by the prototype version of network optimization system are given.

• Journal of Communications and Networks
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• 제14권5호
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• pp.554-565
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• 2012

In this paper, we study a cross-layer and end-to-end optimization problem for the integrated wireless and wireline network that consists of one wireline core network and multiple wireless access networks. We consider joint end-to-end flow control/distribution at the transport and network layers and opportunistic scheduling at the data link and physical layers. We formulate a single stochastic optimization problem and solve it by using a dual approach and a stochastic sub-gradient algorithm. The developed algorithm can be implemented in a distributed way, vertically among communication layers and horizontally among all entities in the network, clearly showing what should be done at each layer and each entity and what parameters should be exchanged between layers and between entities. Numerical results show that our cross-layer and end-to-end optimization approach provides more efficient resource allocation than the conventional layered and separated optimization approach.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2008년도 춘계종합학술대회 A
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• pp.65-68
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• 2008

Mobile IP는 이동 노드에 대한 이동성을 제공할 뿐 네트워크의 이동성은 제공하지 않는다. 네트워크의 이동성을 지원하기 위하여 IETF에서는 NEMO(Network Mobility)를 제안하였다. 이동 네트워크에서는 경로 최적화가 심각한 문제이기 때문에, NEMO WG에서는 주로 중첩된 네트워크 환경에서 경로를 최적화하는 연구가 활발히 이루어지고 있다. 본 논문은 중첩된 이동 네트워크에서 경로 최적화에 관한 방안을 제시하고자 한다.

• Journal of Electrical Engineering and Technology
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• 제12권2호
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• pp.911-917
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• 2017

There are generally three folds when developing neural network classifiers. They are as follows: 1) discriminant function; 2) lots of parameters in the design of classifier; and 3) high dimensional training data. Along with this viewpoint, we propose space search optimized polynomial neural network classifier (PNNC) with the aid of data preprocessing technique and simultaneous tuning strategy, which is a balance optimization strategy used in the design of PNNC when running space search optimization. Unlike the conventional probabilistic neural network classifier, the proposed neural network classifier adopts two type of polynomials for developing discriminant functions. The overall optimization of PNNC is realized with the aid of so-called structure optimization and parameter optimization with the use of simultaneous tuning strategy. Space search optimization algorithm is considered as a optimize vehicle to help the implement both structure and parameter optimization in the construction of PNNC. Furthermore, principal component analysis and linear discriminate analysis are selected as the data preprocessing techniques for PNNC. Experimental results show that the proposed neural network classifier obtains better performance in comparison with some other well-known classifiers in terms of accuracy classification rate.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.421-426
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• 2005

Fuzzy logic, neural network, fuzzy-neural network play an important as the key technology of linguistic modeling for intelligent control and decision making in complex systems. The fuzzy-neural network (FNN) learning represents one of the most effective algorithms to build such linguistic models. This paper proposes particle swarm optimization algorithm based optimal learning fuzzy-neural network (PSOA-FNN). The proposed learning scheme is the fuzzy-neural network structure which can handle linguistic knowledge as tuning membership function of fuzzy logic by particle swarm optimization algorithm. The learning algorithm of the PSOA-FNN is composed of two phases. The first phase is to find the initial membership functions of the fuzzy neural network model. In the second phase, particle swarm optimization algorithm is used for tuning of membership functions of the proposed model.

• 전자공학회논문지B
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• 제31B권7호
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• pp.179-189
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• 1994

A two-phase neural network finds exact feasible solutions for a constrained optimization programming problem. The time-varying programming neural network is a modified steepest-gradient algorithm which solves time-varying optimization problems. In this paper, we propose a time-varying two-phase optimization neural network which incorporates the merits of the two-phase neural network and the time-varying neural network. The proposed algorithm is applied to system identification and function approximation using a multi-layer perceptron. Particularly training of a multi-layer perceptrion is regarded as a time-varying optimization problem. Our algorithm can also be applied to the case where the weights are constrained. Simulation results prove the proposed algorithm is efficient for solving various optimization problems.

• 경영과학
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• 제24권2호
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• pp.127-142
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• 2007

This study deals with the optimal design of mobile access network connecting base stations(BSs) and mobile switching centers(MSCs). Generally mobile operators constitute their access networks by leasing communication lines. Using the characteristic of leased line rate based on administration region, we build an optimization model for mobile access network design which has much smaller number of variables than the existing researches. And we develop a GUI based optimization tool integrating the well-known softwares such as MS EXCEL. MS VisualBasic, MS PowerPoint and Ip_solve, a freeware optimization software. Employing the current access network configuration of a Korean mobile carrier, this study using the optimization tool obtain an optimal solution for both single MSC access network and nation-wide access network. Each optimal access network achieves 7.45% and 9.49% save of lease rate, respectively. Considering the monthly charge and total amount of lease line rate, our optimization tool provides big amount of save in network operation cost. Besides the graphical representation of access networks makes the operator easily understand and compare current and optimal access networks.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 심포지엄 논문집 정보 및 제어부문
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• pp.9-11
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• 2006

This paper presents JPEG quantization table design using RD optimization and neural network. Using R-D optimization, quantization table with good performance can be obtained. However, it is time-consuming and difficult to adopt to embedded systems. In this paper, a new quantization table design method is proposed using R-D optimization and neural network. Neural network learns the quantization table obtained from R-D optimization and produces a quantization table for the Images. The proposed system is applied to Yale face data. From the simulation results, it has been shown that the proposed codec has better performance than JPEG.

• 산업경영시스템학회지
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• 제46권4호
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• pp.53-62
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• 2023

Effects-Based Operations (EBO) refers to a process for achieving strategic goals by focusing on effects rather than attrition-based destruction. For a successful implementation of EBO, identifying key nodes in an adversary network is crucial in the process of EBO. In this study, we suggest a network-based approach that combines network centrality and optimization to select the most influential nodes. First, we analyze the adversary's network structure to identify the node influence using degree and betweenness centrality. Degree centrality refers to the extent of direct links of a node to other nodes, and betweenness centrality refers to the extent to which a node lies between the paths connecting other nodes of a network together. Based on the centrality results, we then suggest an optimization model in which we minimize the sum of the main effects of the adversary by identifying the most influential nodes under the dynamic nature of the adversary network structure. Our results show that key node identification based on our optimization model outperforms simple centrality-based node identification in terms of decreasing the entire network value. We expect that these results can provide insight not only to military field for selecting key targets, but also to other multidisciplinary areas in identifying key nodes when they are interacting to each other in a network.

• 설비공학논문집
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• 제18권8호
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• pp.635-640
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• 2006

An optimizer has been applied for the optimal design of pipe diameters in the pipe flow network problems. Pipe network flow analysis, which is developed separately, is performed within the interface for the optimization algorithm. A pipe network is chosen for the test, and optimizer GenOpt is applied with Holder-Mead-O'Niell's simplex algorithm after solving the network flow problem by the Newton-Raphson method. As a result, optimally do-signed pipe diameters are successfully obtained which minimize the total design cost. Design cost of pipe flow network can be considered as the sum of pipe installation cost and pump operation cost. In this study, a practical and efficient solution method for the pipe network optimization is presented. Test system is solved for the demonstration of the present optimization technique.