• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.94.5-94
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• 2002

The current study of complex dynamical networks is pervading all kinds of sciences today, ranging from physical to biological, even to social sciences. its impact on modern engineering and technology is prominent and will be far-reaching. Typical complex dynamical networks include the World Wide Web, the Internet, various wireless communication networks, meta-bolic networks, biological neural networks, social connection networks, scientific cooperation and citation networks, and so on. Research on fundamental properties and dynamical features of such complex networks have become overwhelm ing. This talk will provide a brief overview of some basic concepts about com plex dynamical netwo...

• 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 applied mathematics & informatics
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• v.30 no.5_6
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• pp.787-792
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• 2012

We present a graphical algorithm and theorems for calculating the ranks of reaction networks. The ranks are needed to study behaviors of the networks from their structures. This approach can graphically simplify complex networks for the calculation. We show an example of a large network for the practical advantage.

• International Journal of Control, Automation, and Systems
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• v.1 no.3
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• pp.289-300
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• 2003

In this paper, we introduce an identification method in Fuzzy Relation-based Fuzzy Neural Networks (FRFNN) through a hybrid identification algorithm. The proposed FRFNN modeling implement system structure and parameter identification in the efficient form of "If...., then... " statements, and exploit the theory of system optimization and fuzzy rules. The FRFNN modeling and identification environment realizes parameter identification through a synergistic usage of genetic optimization and complex search method. The hybrid identification algorithm is carried out by combining both genetic optimization and the improved complex method in order to guarantee both global optimization and local convergence. An aggregate objective function with a weighting factor is introduced to achieve a sound balance between approximation and generalization of the model. The proposed model is experimented with using two nonlinear data. The obtained experimental results reveal that the proposed networks exhibit high accuracy and generalization capabilities in comparison to other models.er models.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.4
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• pp.923-931
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• 2017

A power grid is one of the most complex networks and is critical infrastructure for society. To understand the characteristics of a power grid, complex network analysis has been used from the early 2000s mainly for US and European power grids. However, since the power grids of different countries might have different structures, the Korean power grid needs to be examined through complex network analysis. This paper performs the analysis for the Korean power grid, especially for high-voltage transmission networks. In addition, statistical and small-world characteristics for the Korean power grid are analyzed. Generally, the Korean power grid has similar characteristics to other power grids, but some characteristics differ because the Korean power grid is concentrated in the capital area.

• Smart Structures and Systems
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• v.19 no.4
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• pp.383-391
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• 2017

In this paper, we investigate the impacts of network topology on the performance of a distributed estimation algorithm, namely combine-then-adaptive (CTA) diffusion LMS, based on the data with or without the assumptions of temporal and spatial independence with noisy links. The study covers different network models, including the regular, small-world, random and scale-free whose the performance is analyzed according to the mean stability, mean-square errors, communication cost (link density) and robustness. Simulation results show that the noisy links do not cause divergence in the networks. Also, among the networks, the scale free network (heterogeneous) has the best performance in the steady state of the mean square deviation (MSD) while the regular is the worst case. The robustness of the networks against the issues like node failure and noisier node conditions is discussed as well as providing some guidelines on the design of a network in real condition such that the qualities of estimations are optimized.

• Journal of Internet Computing and Services
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• v.17 no.5
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• pp.1-8
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• 2016

This paper proposes a novel broadcast scheduling algorithm for wireless large-scale networks based on theory of complex networks. In the proposed algorithm, the network topology is formed based on a scale-free network and the probability of link distribution is analyzed. In this paper, the characteristics of complex systems are analyzed (which are not concerned by the existing broadcast scheduling algorithm techniques) and the optimization of network transmission efficiency and network time delay are provided.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.9
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• pp.2020-2028
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• 2013

In this paper, we propose a novel analysis and design methodology based on complex network theory for wireless large-scale ad hoc networks. We also enhance the conventional analysis methods which does not sufficiently consider the effect of the wireless communication channels and extend the existing random graph theory by reflecting the wireless communication environments. As a main result, the effect of the network topology such as average degree of each communication node on the network capacity through extensive computer simulations.

• Journal of Communications and Networks
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• v.16 no.6
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• pp.667-676
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• 2014

Connectivity is a crucial quality of service measure in wireless sensor networks. However, the network is always at risk of being split into several disconnected components owing to the sensor failures caused by various factors. To handle the connectivity problem, this paper introduces an in-advance mechanism to prevent network partitioning in the initial deployment phase. The approach is implemented in a distributed manner, and every node only needs to know local information of its 1-hop neighbors, which makes the approach scalable to large networks. The goal of the proposed mechanism is twofold. First, critical nodes are locally detected by the critical node detection (CND) algorithm based on the concept of maximal simplicial complex, and backups are arranged to tolerate their failures. Second, under a greedy rule, topological holes within the maximal simplicial complex as another potential risk to the network connectivity are patched step by step. Finally, we demonstrate the effectiveness of the proposed algorithm through simulation experiments.

• Journal of Intelligence and Information Systems
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• v.2 no.2
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• pp.43-54
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• 1996

Financial markets are operating 24 hours a day throughout the world and interrelated in increasingly complex ways. Telecommunications and computer networks tie together markets in the from of electronic entities. Financial practitioners are inundated with an ever larger stream of data, produced by the rise of sophisticated database technologies, on the rising number of market instruments. As conventional analytic techniques reach their limit in recognizing data patterns, financial firms and institutions find neural network techniques to solve this complex task. Neural networks have found an important niche in financial a, pp.ications. We a, pp.y neural networks to Standard and Poor's (S&P) 500 stock index futures trading to predict the futures marker behavior. The results through experiments with a commercial neural, network software do su, pp.rt future use of neural networks in S&P 500 stock index futures trading.