• Proceedings of the Korean Information Science Society Conference
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• 2006.10b
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• pp.308-313
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• 2006

EPC Network는 EPC 관련 정보를 수집, 처리, 저장, 제공하는 Network를 말하며 EPC Network에서의 정보 접근 제어는 다양한 접근 방법, 다양한 플랫폼을 사용하는 사용자의 접근 및 분산 환경이라는 상황을 고려해야만 한다. 본 논문에서는 온톨로지를 이용한 간단하면서도 효율적인 정보 접근 제어 기법을 제시하고자 한다. 본 논문에서 제시하는 정보 접근 제어 기법을 간략하게 설명하자면 EPC Network를 구성하는 요소 중 하나인 EPC IS로 전송되는 SOAP 전송 메시지 내부에 정보 접근 제어를 위해 필요한 정보들을 온톨로지를 이용하여 기술한다. EPC IS는 온톨로지를 이용하여 기술된 SOAP 전송 메시지 내부에 포함된 정보 접근 제어와 관련된 정보를 정보 접근 제어 처리에 사용한다. 온톨로지를 이용함으로써 사용자와 EPC IS 간의 개념 및 용어의 일관성을 유지할 수 있으며, 추론 기능을 이용하여 정보 접근 제어에 있어서의 요구 사항들을 쉽게 처리할 수 있다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2011.06a
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• pp.367-368
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• 2011

본 논문에서는 PON(Passive Optical Network)망의 장애진단을 위한 임베디드 네트워크 제어기를 설계하고 구현하였다. 본 연구에서 구현한 임베디드 네트워크 제어기는 PON망의 가장 말단인 수용가의 인터넷 공유기에 부착되어 수용가의 인터넷 선로장애를 진단할 수 있도록 구현함으로서 인터넷 서비스 제공자(ISP)의 NMS가 점검할 수 없는 영역까지 장애를 진단할 수 있다. 또한, 임베디드 네트워크 제어기는 PON망의 장애진단 뿐만 아니라 수용가의 가전제품 전원제어나 다양한 센서를 부착하여 제어할 수 있도록 제작하여 간단한 홈오토메이션 제어기로 활용할 수 있도록 설계하였다.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.18 no.2
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• pp.63-67
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• 2004

We know that Neural Network is in use in many control fields. In time of using as controller, Neural Network controller is needed to learning by Input-output pattern. But in many times of control field. we can not get Input-output pattern of Neural Network controller. As a method solving this problem, in this paper, we try New control method that output node of Neural Network bringing control object. Such a New control method application, we can solve the data taking problem to Neural Network controller Input-output. The effectiveness of proposed control algorithm is verified by simulation results of DC servo motor.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.18 no.1
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• pp.85-89
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• 2004

Robust control for feedback control system is needed according to the highest precision of industrial automation. However, when a neural network feedback control system has an effect of disturbance, it is very difficult to guarantee the robustness of control system. As a compensation method solving this problem, in this paper, hybrid control method of neural network controller and PID controller is presented. A neural network controller is operated as a main controller, a PID controller is a assistant controller which operates only when some undesirable phenomena occur, e.q., when the error hit the boundary of constraint set. The robust control function of neural network-PID hybrid controller is demonstrated by speed control of Motor.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.5
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• pp.51-59
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• 1997

In this paper, we perform the position control of a DC servo motor using fuzzy neural controller. We use the Fuzzy controller for the position control, because the Fuzzy controller is designed simpler than other intelligent controller, but it is difficult to design for the triangle membership function format. Therefore we solve the problem using the BP learning method of neural network. The proposed Fuzzy neural network controller has been applied to the position control of various virtual plants. And the DC servo motor position control using the fuzzy neural network controller is performed as a real time experiment.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.12 no.1
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• pp.111-116
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• 1998

Robust control for DC servo motor is needed according to the highest precision of industrial automation. However, when a motor control system with PID controller has an effect of load disturbance, it is very difficult to guarantee the robustness of control system. As a compensation method solving this problem, in this paper, PID-neural network hybrid control method for motor control system is presented. The output of neural network controller is determined by error and rate of error change occurring in load disturbance. The robust control of DC servo motor using neural network controller is demonstrated by computer simula tion.a tion.

• Journal of the Korea Society of Computer and Information
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• v.26 no.1
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• pp.103-110
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• 2021

In this paper, we compare two access control mechanisms for D2D(Device-to-Device) systems in 5G wireless networks and propose an effective access control for 5G D2D networks. Currently, there is no specified access control for 5G D2D networks but there can be two access control approaches for 5G D2D networks. One is the UE-to-Network Relay based access control and the other is the Remote UE(User Equipment) based access control. The former is a UE-to-Network Relay carries out the access control check for 5G D2D networks but the latter is a Remote UE performs the access control check for 5G D2D networks. Through simulation and evaluation, we finally propose the Remote UE based access control for D2D systems in 5G wireless networks. The proposed approach minimizes signalling overhead between the UE-to-Network Relay and the Remote UE and more efficiently performs the access control check, when the access control functionalities are different from the UE-to-Network Relay in 5G D2D networks.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.16 no.7
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• pp.610-614
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• 1991

This paper proposes a trajectory constrol fo a robot manipulator by using neural network. The inverse dynamic model of manipuator is learned by neural network. The manipulator is controlled by weight values of the learned neural network. The weight valuese is change with a torque of liner vontroller and a acceleration error. Phsically, the totlal torque for a manipualator is a sum of the liner controller torque and the nerural network controller torque. The proposed control effect is estimated by computer simulation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.12
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• pp.2365-2370
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• 2008

In this paper, applications of wavelet neural network controller to position control of nonlinear system are considered. Wavelet neural network is used in the objectives which improve the efficiency of LQR controllers. It is possible to make unstable nonlinear systems stable by using LQR(Linear Quadratic Regulator) technique. And, in order to be adapted to disturbance effectively in this system it uses wavelet neural network controller. Applying this method to the position control of nonlinear system, its usefulness is verified from the results of experiment.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.41 no.3
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• pp.39-46
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• 2004

This paper is proposed adaptive fuzzy-neural network(FNN) controller for the speed control of interior permanent magnet synchronous motor(IPMSM) drive. The design of this algorithm based on FNN controller that is implemented by using fuzzy control and neural network. This controller uses fuzzy rule as training patterns of a neural network. Also, this controller uses the back-propagation method to adjust the weights among the neurons of neural network in order to minimize the error between the command output and actual output. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed by fuzzy logic based on the error and change of error measured between the motor speed and output of a reference model. The control performance of the adaptive FNN controller is evaluated by analysis for various operating conditions. The results of analysis prove that the proposed control system has strongly high performance and robustness in parameter variation, steady-state accuracy and transient response.