• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 1995.11a
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• pp.320-323
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• 1995

In this paper, in order to improve withstand voltage properties of epoxy resin, IPN(interpenetrating polymer network) method was introduced and the influence was investigated. The sing1e network structure specimen(E series), simultaneous interpenetrating polymer network specimen(EMF series) and pseudo interpenetrating polymer network(EMP series) specimen were manufactured. In order to understand the internal structure properties, scanning electron microscopy method was utilized, rind glass transition temperature was measured. Also, AC voltage dielectric strength, tensile strength and impact strength were measured to investigate influence upon electrical and mechanical properties. As a result, it was confirmed that simultaneous interpenetrating polymer network specimen was the most execellent.

• Proceedings of the KIEE Conference
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• 2005.07d
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• pp.2681-2683
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• 2005

In this paper, we present the structure of fuzzy neural network(FNN) based on wavelet function, and apply this network structure to the identification of nonlinear systems. For adjusting the shape of membership function and the connection weights, the parameter learning method based on the gradient descent scheme is adopted. And an approach that uses adaptive learning rates is driven via a Lyapunov stability analysis to guarantee the fast convergence. Finally, to verify the efficiency of our network structure. we compare the Identification performance of proposed wavelet based fuzzy neural network(WFNN) with those of the FNN, the wavelet fuzzy model(WFM) and the wavelet neural network(WNN) through the computer simulation.

• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.1832-1833
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• 2006

This paper presents a design method of the wavelet neural network(WNN) controller based on a direct adaptive control scheme for the intelligent control of Autonomous Underwater Vehicle(AUV) steering systems. The neural network is constructed by the wavelet orthogonal decomposition to form a wavelet neural network that can overcome nonlinearities and uncertainty. In our control method, the control signals are directly obtained by minimizing the difference between the reference track and original signal of AUV model that is controlled through a wavelet neural network. The control process is a dynamic on-line process that uses the wavelet neural network trained by gradient-descent method. Through computer simulations, we demonstrate the effectiveness of the proposed control method.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1581-1585
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• 2005

This paper describes the design and implementation of a XCP (Xeline Control Protocol) network system. XCP is an information oriented protocol which delivers information with high reliability according to the predefined rule. The XCP network system is implemented with partly hardware and partly software based on the power line communication(PLC) environment. A network management tool which interacts with devices is also developed. In order to verify the feasibility of the proposed architecture, the implemented XCP network system is evaluated using a lighting control system.

• Proceedings of the IEEK Conference
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• 2002.06e
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• pp.169-172
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• 2002

Web-based virtual machine/manufacturing system (VMS) utilizes Virtual Device Network (VDN.) VDN inevitably involves the implementation of Distributed Monitoring and Control Networks (DMCN). In general, one needs to integrate device (control) network and IP network to realize DMCN over IP network or internet, which can be viewed as a VDN. In this study, LonWorks networking technology is used fer device network and the transmission characteristics of LonWorks/IP-based VDN is investigated. A method to minimize the transmission delay in the LonWorks/IP networks is also suggested.

• Proceedings of the KIPE Conference
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• 2002.07a
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• pp.693-696
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• 2002

In this paper, network management system(NMS) was developed using Ethernet network for several devices. Recently, due to the development of the information communication, network has been constructed several place. And management system using network has been studied due to the increment of necessity of remote control for industrial device. Agent board that necessity of NMS, was developed using general micro-controller, it operates like stand-alone network device, supports TCP/IP protocol suite, has the ability to connect to industrial device and communicates each other. Also manager base on MMI was developed, it operates with agent board and supports effective management. To prove this system UPS(uninterruptible power supply) is selected as the example of industrial device. Finally, experimental result verifies the communication between agent board and manager.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.28 no.11
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• pp.51-57
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• 2014

This paper presents an algorithmic type computing technique of process coefficient in predicting model of temperature for reheating furnace and also suggests a design method of neural network model to find an adequate value of process coefficient for arbitrary operating conditions including test conditons. The proposed neural network use furnace temperature, line speed and slab information as input variables, and process coefficient is output variable. Reasonable process coefficients can be obtained by an algorithmic procedure proposed in this paper using process data gathered at test conditons. Also, neural network model output equal process coefficient under same input conditions. This means that adquate process coefficients can be found by only computing neural network model without additive test even if operating conditions vary.

• Proceedings of the KIEE Conference
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• 1998.07g
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• pp.2293-2295
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• 1998

In this paper, an improved on-line neural network model is suggested. This neural network is designed to store and recall sequence of key strokes in on-line. The network stores incoming patterns as weight connections between series of layers. The layer has a 2-dimensionally distributed neurons where the location of neurons are relevant to the actual location of computer keyboard. To store longer patterns, the network has circulating layer connections and different patterns can be superposed on the same layer. Also, when the patterns are stored over the layers, the starting layer is not fixed but changed by the characteristics of Patterns to increases network capability. The ways how to choose the starting layer during the store and recall process are investigated.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.199-205
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• 2022

The extensive growth in data rate demand by the smart gadgets and mobile broadband application services in wireless cellular networks. To achieve higher data rate demand which leads to aggressive frequency reuse to improve network capacity at the price of Inter Cell Interference (ICI). Fractional Frequency Reuse (FFR) has been recognized as an effective scheme to get a higher data rate and mitigate ICI for perfect geometry network scenarios. In, an irregular geometric multicellular network, ICI mitigation is a challenging issue. The purpose of this paper is to develop distributed dynamic power allocation scheme for FFR based on game theory to mitigate ICI. In the proposed scheme, each cell region in an irregular multicellular scenario adopts a self-less behavior instead of selfish behavior to improve the overall utility function. This proposed scheme improves the overall data rate and mitigates ICI.

• ETRI Journal
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• v.44 no.1
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• pp.155-167
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• 2022

The Internet of Things (IoT) is a new paradigm that connects physical and virtual objects from various domains such as home automation, industrial processes, human health, and monitoring. IoT sensors receive information from their environment and forward it to their neighboring nodes. However, the large amounts of exchanged data are vulnerable to attacks that reduce the network performance. Most of the previous security methods for IoT have neglected the energy consumption of IoT, thereby affecting the performance and reducing the network lifetime. This paper presents a new multistep routing protocol based on cellular learning automata. The network lifetime is improved by a performance-based adaptive reward and fine parameters. Nodes can vote on the reliability of their neighbors, achieving network reliability and a reasonable level of security. Overall, the proposed method balances the security and reliability with the energy consumption of the network.