• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.8
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• pp.292-298
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• 2013

This paper considers synchronization problem of a complex dynamical network with nonidentical nodes. For the problem, the target node is chosen as one of nodes in the complex network instead of an isolate node. Moreover, our synchronization scheme does not need additional conditions and information of coupling matrix comparing with existing works. Based on Lyapunov stability theory, a design criterion for a novel adaptive feedback controller for the synchronization between the target node and another nodes of the complex network is proposed. Finally, the proposed method is applied to a numerical example in orther to show the effectiveness of our results.

• 연구논문집
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• s.26
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• pp.69-84
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• 1996

Regional INformation Network Center has constructed integrated VAN among the heterogeneous computers and core technical information database for machinery & materials-oriented enterprises in the Changwon & Masan Industrial complex. As the result of RINNet project, regional industrial companies can take advantage of getting core technical information for developing new technology and strengthening competitive power. Accordingly, RINNet(Regional INformation Network for science & technology) makes regional industrial companies possible to collect, acquire and use technical information rapidly and motivates regional society to form the rapid technical interchange system and cooperative system among industrial-educational-institutal complex.

• Nuclear Engineering and Technology
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• v.56 no.8
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• pp.2923-2936
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• 2024

Modern risk management philosophy emphasizes the invulnerability of human beings to cope with all kinds of emergencies. The Nuclear Accidents Emergency Response Organization (NAERO) of Nuclear Power Plant (NPP) is the primary body responsible for nuclear accidents emergency response. The invulnerability of the organization to disturbance or attack from internal and external sources is crucial in the completion of its response missions, reduction of severity of accidents, and assurance of public and environmental safety. This paper focused on the NAERO of a certain NPP in China, and applied the complex network theory to construct the network model of the organization. The topological characteristics of the network were analyzed. Four importance evaluation indexes of network nodes including Degree Centrality (DC), Betweeness Centrality (BC), Closeness Centrality (CC) and Eigenvector Centrality (EC), along with Pearson coefficient correlation among the indexes were calculated and analyzed. Size of the Largest Connected Component (LCC) and Network Efficiency were used as measures regarding the invulnerability of the network. Simulation experiments were conducted to assess the invulnerability of network against various attack strategies. These experiments were conducted both in the absence of node protection measures and under protection measures with different node protection rates. This study evaluated the invulnerability of the NAERO network, and provided significant decision-making basis for the enhancement of the network's invulnerability.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.2
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• pp.35-44
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• 1998

This paper describes multiple fault diagnosis method in complex system with hierarchical structure. Complex system is divided into subsystem, item and component. For diagnosing this hierarchical complex system, it is necessary to implement special neural network. We introduced Modular Artificial Neural Network(MANN) for this purpose. MANN consists of four level neural network, first level for symptom classification, second level for item fault diagnosis, third level for component symptom classification, forth level for component fault diagnosis. Each network is multi layer perceptron with 7 inputs, 30 hidden node and 7 outputs trained by backpropagation. UNIX IPC(Inter Process Communication) is used for implementing MANN with multitasking and message transfer between processes in SUN workstation. We tested MANN in reactor system.

• Journal of the Korean Society of Industry Convergence
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• v.7 no.2
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• pp.199-205
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• 2004

This paper describes the fault diagnosis method to order to construct CIM in complex system with hierarchical structure similar to human body structure. Complex system is divided into unit, item and component. For diagnosing this hierarchical complex system, it is necessary to implement a special neural network. Fault diagnosis system can forecast faults in a system and decide from the signal information of current machine state. Comparing with other diagnosis system for a single fault, the developed system deals with multiple fault diagnosis, comprising hierarchical neural network (HNN). HNN consists of four level neural network, i.e. first is fault symptom classification and second fault diagnosis for item, third is symptom classification and forth fault diagnosis for component. UNIX IPC is used for implementing HNN with multitasking and message transfer between processes in SUN workstation with X-Windows (Motif). We tested HNN at four units, seven items per unit, seven components per item in a complex system. Each one neural network represents a separate process in UNIX operating system, information exchanging and cooperating between each neural network was done by message queue.

• International Journal of Computer Science & Network Security
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• v.21 no.12spc
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• pp.643-647
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• 2021

The use of data analysis on large volumes of data constitutes a challenge for deducting knowledge and new information. Data can be heterogeneous and complex: Semi-structured data (Example: XML), Data from social networks (Example: Tweets) and Factual data (Example: Spreading of Covid-19). In this paper, we propose a generic multidimensional model in order to analyze complex data, according to several dimensions.

• Journal of Korea Game Society
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• v.2 no.2
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• pp.46-51
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• 2002

In this study, a real-time simulation method for the phenomena, which are too complex to be simulated during real-time computer games, was proposed based on the neural network. The procedure of proposed method is to 1) obtain correlation data between input parameters and output parameters by mathematical modeling, code analyses, and so on, 2) train the neural network with the correlation data, 3) and insert the trained neural network in a game program as a simulation module. For the case that the number of the input and output parameters is too high to be analyzed, a method was proposed to omit parameters of little importance. The method was successfully applied to severe accidents of nuclear power plants, reflecting that the method was very effective in real time simulation of complex phenomena.

• The Journal of the Acoustical Society of Korea
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• v.41 no.1
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• pp.30-37
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• 2022

This paper compares and evaluates model performance from two perspectives according to the learning target and network structure for training Deep Neural Network (DNN)-based speech enhancement models in the frequency domain. In this case, spectrum mapping and Time-Frequency (T-F) masking techniques were used as learning targets, and a real network and a complex network were used for the network structure. The performance of the speech enhancement model was evaluated through two objective evaluation metrics: Perceptual Evaluation of Speech Quality (PESQ) and Short-Time Objective Intelligibility (STOI) depending on the scale of the dataset. Test results show the appropriate size of the training data differs depending on the type of networks and the type of dataset. In addition, they show that, in some cases, using a real network may be a more realistic solution if the number of total parameters is considered because the real network shows relatively higher performance than the complex network depending on the size of the data and the learning target.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.4
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• pp.1392-1403
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• 2015

The rapid development of both communication traffic and increasing optical network sizes has increased energy consumption. Traditional algorithms and strategies don't apply to controlling the expanded network. Immunization algorithms originated from the complex system theory are feasible for large-scale systems based on a scale-free network model. This paper proposes the immunization strategy for complex systems which includes random and targeted immunizations to solve energy consumption issues and uses traffic to judge the energy savings from the node immunization. The simulation results verify the effectiveness of the proposed strategy. Furthermore, this paper provides a possibility for saving energy with optical transmission networks.

• The Korean Journal of Applied Statistics
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• v.36 no.5
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• pp.361-380
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• 2023

Magnetoencephalography (MEG) is a technique to record oscillatory magnetic fields coming from ongoing neuronal activity. Functional brain activities performing cognitive or physiological tasks are performed on structural connections between neurons or brain regions. MEG data can be characterized as highly correlated, spatio-temporal, multidimensional, multilayered dynamic networks. Due to its complex structure, many studies on MEG network have not yet been conducted. In this study, we will explain the concept, necessity, and possible approaches of MEG network analysis. We reviewed the characteristics of MEG data. Network measures and potential network models in MEG and clinical studies are also reviewed.