• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.3
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• pp.1063-1082
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• 2018

The concept of SDN (Software Defined Networking) endows the network with programmability and significantly improves the flexibility and extensibility of networks. Currently a plenty of research works on introducing SDN into wireless networks. Most of them focus on the innovation of the SDN based architectures but few consider how to realize the global perception of the network through the controller. In order to address this problem, a software defined carrier grade Wi-Fi framework called SWAN, is proposed firstly. Then based on the proposed SWAN architecture, a blueprint of introducing the traditional NSA (Network Situation Awareness) into SWAN is proposed and described in detail. Through perceiving various network data by a decentralized architecture and making comprehension and prediction on the perceived data, the proposed blueprint endows the controllers with the capability to aware of the current network situation and predict the near future situation. Meanwhile, the extensibility of the proposed blueprint makes it a universal solution for software defined wireless networks SDWNs rather than just for one case. Then we further research one typical use case of proposed NSA blueprint: network performance awareness (NPA). The subsequent comparison with other methods and result analysis not only well prove the effectiveness of proposed NPA but further provide a strong proof of the feasibility of proposed NSA blueprint.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.426-428
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• 2004

Sensor network supports data delivery from physical world to cyber space Sensors get physical events then wireless network transfers sensor data to service server. We use sensor network technology to manage location information of asset. In ubiquitous computing environment, user localization is basic context for intelligent service. A lot of research group make effort to develop low cost localization technology. In this paper, we propose asset monitoring system using wireless sensor network. It is implemented using ad hoc network technology which can be adopted to smart home and this system can monitor the asset location and movement.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.5
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• pp.120-128
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• 1993

In this paper, an implementation of high-speed parallel processing system for neural network design on the multicomputer network is presented. Linear speedup expandability is increased by reducing the synchronization penalty and the communication overhead. Also, we presented the parallel processing models and their performance evaluation models for each of the parallization methods of the neural network. The results of the experiments for the character recognition of the neural network bases on the proposed system show that the proposed approach has the higher linear speedup expandability than the other systems. The proposed parallel processing models and the performance evaluation models could be used effectively for the design and the performance estimation of the neural network on the multicomputer network.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2419-2422
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• 2000

A distributed real-time system that is being used now is usually divided into three level : higher level, middle level, and lower level. The higher level network is usually called an information network, the middle level is called a control network, and the lower level is called a field network or a divice network. This dissertation suggests and implements a middle level network which is called PICNET-NP (Plant Implementation and Control Network for Nuclear Power Plant). PICNET-NP is based partly on IEEE 802.4 token-passing bus access methed and partly on IEEE 802.3 physical layer. For this purpose a new interface, a physical layer service translater, is introduced. A control network using this method is implemented and applied to a distributed real-time system.

• Proceedings of the Korea Society for Energy Engineering kosee Conference
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• 1999.11a
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• pp.143-148
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• 1999

A distributed real-time system that is being wed now is usually divided into three level : higher level, middle level, and lower level. The higher level network is usually called an information network, the middle level is called a control network, and the lower level is called a field network or a divice network. This dissertation suggests and implements a middle level network which is called PICNET-NP (Plant Implementation and Control Network for Nuclear Power Plant). PICNET-NP is based partly on IEEE 802.4 token-passing bus access method and partly on IEEE 802.3 physical layer. For this purpose a new interface, a physical layer service translator, is introduced. A control network using this method is implemented and applied to a distributed real-time system.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.635-639
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• 2004

In this paper, we present a novel approach for the structure of Fuzzy Neural Network(FNN) based on wavelet function and apply this network structure to the modeling of chaotic nonlinear systems. Generally, the wavelet fuzzy model(WFM) has the advantage of the wavelet transform by constituting the fuzzy basis function(FBF) and the conclusion part to equalize the linear combination of FBF with the linear combination of wavelet functions. However, it is very difficult to identify the fuzzy rules and to tune the membership functions of the fuzzy reasoning mechanism. Neural networks, on the other hand, utilize their learning capability for automatic identification and tuning. Therefore, we design a wavelet based FNN structure(WFNN) that merges these advantages of neural network, fuzzy model and wavelet transform. The basic idea of our wavelet based FNN is to realize the process of fuzzy reasoning of wavelet fuzzy system by the structure of a neural network and to make the parameters of fuzzy reasoning be expressed by the connection weights of a neural network. And our network can automatically identify the fuzzy rules by modifying the connection weights of the networks via the gradient descent scheme. To verify the efficiency of our network structure, we evaluate the modeling performance for chaotic nonlinear systems and compare it with those of the FNN and the WFM.

• THE INTERNATIONAL COMMERCE & LAW REVIEW
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• v.15
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• pp.239-261
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• 2001

There are many kinds of risk in int'l trade by internet network, such as credit risk, mercantile risk, contingency risk, exchange risk, physical risk and the risk on internet network. Especially, risk management against credit risk and the risk on internet network are very important. The former is conventional but more important these days. The latter is a new risk that has been incurred owing to the int'l trade by internet network. The system of risk management against the former are firstly, to surely research credit of counterpart by internet, secondly, to certify the entity by password or fingerprint, thirdly, to pay the price under a letter of credit, fourthly, to use the system of int'l trade such as bolero, trade card, finally, to use the authority of electronic trade services. The system of risk management against the latter are firstly, to install the firewall on the own computer network, secondly, to entrust the management own computer network to the network security services firm, thirdly, to electronically communicate with counterpart through the certification authority, finally, to insure against the own network risk with the security insurance company.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.1
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• pp.76-82
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• 2005

Speech signal has various features of speakers. This feature is extracted from speech signal processing. The speaker is identified by the speaker identification system. In this paper, we propose the speaker identification system that uses the incremental learning based on neural network. Recorded speech signal through the microphone is blocked to the frame of 1024 speech samples. Energy is divided speech signal to voiced signal and unvoiced signal. The extracted 12 orders LPC cpestrum coefficients are used with input data for neural network. The speakers are identified with the speaker identification system using the neural network. The neural network has the structure of MLP which consists of 12 input nodes, 8 hidden nodes, and 4 output nodes. The number of output node means the identified speakers. The first output node is excited to the first speaker. Incremental learning begins when the new speaker is identified. Incremental learning is the learning algorithm that already learned weights are remembered and only the new weights that are created as adding new speaker are trained. It is learning algorithm that overcomes the fault of neural network. The neural network repeats the learning when the new speaker is entered to it. The architecture of neural network is extended with the number of speakers. Therefore, this system can learn without the restricted number of speakers.

• Journal of Korea Society of Digital Industry and Information Management
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• v.7 no.3
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• pp.97-105
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• 2011

With the advancement of network, privacy information as well as confidential information that belongs to government and company are exposed to security incident like spreading viruses or DDoS attack. To prevent these security incident and protect information that belongs to government and company, Security system has developed such as antivirus, firewall, IPS, VPN, and other network security system. Network security systems should be selected based on purpose, usage and cost. Verification for network security product's basic features performed in a variety of ways at home and abroad, but consumers who buy these network security product, just rely on the information presented at companies. Therefore, common user doing self performance evaluation for perform Verification before buying network security product but these verification depends on inaccurate data which based on some user's criteria. On this paper, we designing methodology of network security system performance evaluation focused on Korean using other cases of performance evaluation.

• Journal of the Institute of Convergence Signal Processing
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• v.9 no.3
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• pp.238-244
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• 2008

Dynamic neural networks have been applied to diverse fields requiring temporal signal processing. This paper presents gamma neural network(GAM) to improve the dynamics of multilayer network. The GAM network uses the gamma memory kernel in the hidden layer of feedforword multilayer network. The GAM network is evaluated in linear and nonlinear system identification, and compared with feedforword(FNN) and recurrent neural networks(RNN) for the relative comparison of its performance. Experimental results show that the GAM network performs better with respect to the convergence and accuracy, indicating that it can be a more effective network than conventional multilayer networks in system identification.