• The Journal of Korean Institute of Communications and Information Sciences
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• v.17 no.3
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• pp.225-231
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• 1992

In this paper, queuing networks with open chains are considerd to analyze the performance of packet switching networks. Networks are classified into backbone and local access networks. Networks for performance analysis are distributed to twelve regions and DNS is the backbone. Analysis was conducted using the real values from the input to existing networks and mathematical estimation values. As the result of analysis, the mean of end-to-and delay for each chain was presented. Except special regions, we found that there was a little difference between real values and mathematical estimation values. However, there could be a performance problem in total networks due to the increase of communication volumes in each region. So we proposed some solutions to this problem.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.10a
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• pp.31-34
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• 1996

The extensive researches have compared the performance of neural networks(NN) with those of various statistical techniques for the classification problem. The empirical results of these comparative studies have indicated that the neural networks often outperform the traditional statistical techniques. Moreover, there are some efforts that try to combine various classification methods, especially multivariate discriminant analysis with neural networks. While these efforts improve the performance, there exists a problem violating robust assumptions of multivariate discriminant analysis that are multivariate normality of the independent variables and equality of variance-covariance matrices in each of the groups. On the contrary, cluster analysis alleviates this assumption like neural networks. We propose a new approach to classification problems by combining the cluster analysis with neural networks. The resulting predictions of the composite model are more accurate than each individual technique.

• Journal of Information Processing Systems
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• v.14 no.2
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• pp.455-468
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• 2018

The concepts of graph theory are applied to model and analyze dynamics of computer networks, biochemical networks and, semantics of social networks. The analysis of dynamics of complex networks is important in order to determine the stability and performance of networked systems. The analysis of non-stationary and nonlinear complex networks requires the applications of ordinary differential equations (ODE). However, the process of resolving input excitation to the dynamic non-stationary networks is difficult without involving external functions. This paper proposes an analytical formulation for generating solutions of nonlinear network ODE systems with functional decomposition. Furthermore, the input excitations are analytically resolved in linearized dynamic networks. The stability condition of dynamic networks is determined. The proposed analytical framework is generalized in nature and does not require any domain or range constraints.

• Journal of the Korean Society for Precision Engineering
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• v.28 no.7
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• pp.814-820
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• 2011

It takes long time in numerical simulation because structural design for tire requires the nonlinear material property. Neural networks has been widely studied to engineering design to reduce numerical computation time. The numbers of hidden layer, hidden layer neuron and training data have been considered as the structural design variables of neural networks. In application of neural networks to optimize design, there are a few studies about arrangement method of input layer neurons. To investigate the effect of input layer neuron arrangement on neural networks, the variables of tire contour design and tension in bead area were assigned to inputs and output for neural networks respectively. Design variables arrangement in input layer were determined by main effect analysis. The number of hidden layer, the number of hidden layer neuron and the number of training data and so on have been considered as the structural design variables of neural networks. In application to optimization design problem of neural networks, there are few studies about arrangement method of input layer neurons. To investigate the effect of arrangement of input neurons on neural network learning tire contour design parameters and tension in bead area were assigned to neural input and output respectively. Design variables arrangement in input layer was determined by main effect analysis.

• Structural Engineering and Mechanics
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• v.6 no.5
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• pp.517-527
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• 1998

This paper describes an exact method of analysis for natural vibration of diagonal networks by considering an equivalent cyclic periodic structure and adopting the double U-transformation technique. Both a lumped mass system and a distributed mass system are considered to investigate the diagonal networks. The exact solution for the frequency equations and the natural modes of the networks can be derived. As numerical examples, square diagonal cable networks with different meshes are worked out.

• Journal of the Korean Institute of Landscape Architecture
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• v.34 no.4 s.117
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• pp.18-36
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• 2006

The purpose of this research was to establish a green-networks from the perfective of landscape ecology in order to improve the function of urban green spaces. The study site was Dalsu-Gu in Daegu City. This research consisted of three phases. In the first phase, field surveys were carried out in order to understand existing distribution pattern of green spaces in the study site. 533 green spaces surveyed in the first phase were classified into 7 patterns and 24 types. The total area of the green spaces in Dalsu-gu was 3,329ha. Specifically the area of the 'urban nature parks' type was 57.49% of the total area of green spaces in Dalsu-gu, and it was expected that 'urban nature parks' type can play important roles in the green-networks in Dalsu-gu. Two analysis with green spaces in 9 types including 'urban nature parks', 'rivers' and 'neighborhood parks' were performed to establish a basic network frame of the green-networks. In the second phase, 'mutual connectivity analysis' and 'mutual matrix analysis' were performed to select core green spaces of a green-networks using 'areas of each green space and a distance between each space' and 'a rate of green spaces and a rate of water permeable pavement'. The results of the second phase indicated that, in mutual connectivity analysis, large green spaces apart from each other were evaluated as having higher mutual connectivity than small green spaces near to each other. In mutual matrix analysis, the green spaces with higher mutual connectivity and the small green spaces near to each other were evaluated as having better mutual matrix. In the last phase, we structured a basic frame of the green-networks in Dalsu-Gu. The results suggested that the basic frame of the green-networks in Dalsu-Gu was composed on four green-network axes and its shape mirrored a cruciform(+) of northwest${\longleftrightarrow}$southeast directions and southwest${\longleftrightarrow}$northeast directions, The Duryu neighborhood park is at the central point of this green-networks.

• Computational Structural Engineering
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• v.10 no.2
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• pp.265-272
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• 1997

The purpose of this paper is to develop Neural Network models for Approximate Structural Analysis (NNASA). As an initial stage, the paper classifies the characteristics and the active role of neural networks in the numerical analysis by comparing neural networks with conventional numerical analysis algorithms. The paper proposed two methods of finding solutions of linear algebraic equations by a modified neural network algorithm, and presents that multilayer feedforward networks are a class of universal approximators by comparing the neural network with regression and interpolation techniques.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.51 no.8
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• pp.409-416
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• 2002

This paper presents a fault location algorithm using direct 3-phase circuit analysis for distribution power networks. The unbalanced feature of distribution networks due to single phase loads or asymmetric operation prohibits us from using the conventional symmetrical component transformation. Even though the symmetrical component transformation provides us with a very easy tool in three phase network analysis, it is limited to balanced systems in utilizing its strong point, which is not suitable for distribution networks. In this paper, a fault location algorithm using direct 3-phase circuit analysis is developed. The algorithm is derived and it Is shown that the proposed method if we use matrix inverse lemma, is not more difficult then the conventional methods using symmetrical component transformation. Since the symmetrical component transformation is not used in the suggested method, unbalanced networks also can be handled with the same difficulty as balanced networks. The case study results show the correctness and effectiveness of the proposed algorithm.

• 전기의세계
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• v.23 no.3
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• pp.70-76
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• 1974

Computer is used to analyze nonlinear networks. Integrated circuits and new nonlinear elements have generated much interest in nonlinear circuit theory. A key to the understanding and analysis of nonlinear circuits is the study of characteristics for nonlinear elements and nonlinear resistive networks both in theory and in computation. In this apper, an iteration method using cut set analysis for nonlinear dc analysis based on Branin's method is described. Application of this algorithm to solve two nonlinear problems, is presented and a possible method of improving the basic algorithm by means of a sparse matrix technique is described.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.51 no.9
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• pp.467-473
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• 2002

In this paper, a fault location algorithm is suggested for line to line faults in distribution networks. Conventional fault location algorithms use the symmetrical component transformation, a very useful tool for transmission network analysis. However, its application is restricted to balanced network only. Distribution networks are, in general, operated in unbalanced manners, therefore, conventional methods cannot be applied directly, which is the reason why there are few research results on fault location in distribution networks. Especially, the line to line fault is considered as a more difficult subject. The proposed algorithm uses direct 3-phase circuit analysis, which means it can be applied not only to balanced networks but also to unbalanced networks like distribution a network. The comparisons of simulation results between one of conventional methods and the suggested method are presented to show its effectiveness and accuracy.