• 한국정보통신설비학회:학술대회논문집
• /
• 한국정보통신설비학회 2007년도 학술대회
• /
• pp.19-21
• /
• 2007

디지털용 Uncooled DFB 레이저 다이오드를 이용하여 광대역 아날로그 피드포워드 광송신기를 제작하였다. 광송신기의 상호변조왜곡 성분 및 잡음성분을 제거하기 위해 광 피드포워드 보상기법을 사용하였으며, 다중서비스를 위해 $2.05{\sim}2.60$ GHz(550 MHz)에서 상호 변조왜곡 성분이 10 dB 이상 억제되었고, 상대강도잡음은 1.5 dB 이상 억제되었다. 2.3 GHz 에서 3 차 상호변조왜곡성분이 21.3 dB, SFDR 이 7.11 dB 향상된 결과를 얻었다. 또한 단일 모드 광섬유 전송 실험을 통해 전송길이에 따른 3 차 상호변호 왜곡성분의 크기 변화를 확인하였다.

• 대한산업공학회지
• /
• 제27권3호
• /
• pp.305-314
• /
• 2001

In this paper, we construct an automatic evaluation system of psychological feeling for corporate identity (CI) symbol mark based on a fuzzy neural network technique. The system is modelled by trainable fuzzy inference rules with several input variables (qualitative and quantitative design components of CI symbol mark) and a single output variable (consumer's feeling). The back propagation learning algorithm, which is a conventional learning method of multilayer feedforward neural networks, is used for parameter identification of the fuzzy inference system. The learning ability to train data and the generalization ability to test data are evaluated for the proposed evaluation system by computer simulations.

• Journal of Electrical Engineering and information Science
• /
• 제3권2호
• /
• pp.174-183
• /
• 1998

A simple and robust digital current control technique for a permanent magnet (PM) synchronous motor under the parameter variations is presented. Among the various current control schemes for an inverter-fed PM synchronous motor drive, the predictive control is known to give a superior performance. This scheme, however, requires the full knowledge of machine parameters and operating conditions, and cannot give a satisfactory response under the parameter mismatch. To overcome such a limitation, the disturbances caused by the parameter variations will be estimated by using a disturbance observer theory and used for the computation of the reference voltages by a feedforward control. Thus, the steady-state control performance can be significantly improved with a relatively simple control algorithm, while retaining the good characteristics of the predictive control. The proposed control scheme is implemented on a PM synchronous motor using the software of DSP TMS320C30 and the effectiveness is verified through the comparative simulations and experiments.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2005년도 ICCAS
• /
• pp.2521-2525
• /
• 2005

In this paper, we presented a predictive control technique, which is based on wavelet neural network (WNN), for the control of chaotic systems whose precise mathematical models are not available. The WNN is motivated by both the multilayer feedforward neural network definition and wavelet decomposition. The wavelet theory improves the convergence of neural network. In order to design predictive controller effectively, the WNN is used as the predictor whose parameters are tuned by error between the output of actual plant and the output of WNN. Also the training method for the finding a good WNN model is the Extended Kalman algorithm which updates network parameters to converge to the reference signal during a few iterations. The benefit of EKF training method is that the WNN model can have better accuracy for the unknown plant. Finally, through computer simulations, we confirmed the performance of the proposed control method.

• Journal of information and communication convergence engineering
• /
• 제1권4호
• /
• pp.177-182
• /
• 2003

Conventional multilayer feedforward artificial neural networks are very effective in dealing with spatial problems. To deal with problems with time dependency, some kinds of memory have to be built in the processing algorithm. In this paper we show how the newly proposed Serial Input Neuron (SIN) convolutional decoders can be derived. As an example, we derive the SIN decoder for rate code with constraint length 3. The SIN is tested in Gaussian channel and the results are compared to the results of the optimal Viterbi decoder. A SIN approach to decode convolutional codes is presented. No supervision is required. The decoder lends itself to pleasing implementations in hardware and processing codes with high speed in a time. However, the speed of the current circuits may set limits to the codes used. With increasing speeds of the circuits in the future, the proposed technique may become a tempting choice for decoding convolutional coding with long constraint lengths.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 1995년도 Proceedings of the Korea Automation Control Conference, 10th (KACC); Seoul, Korea; 23-25 Oct. 1995
• /
• pp.1-4
• /
• 1995

We propose a new design method for a generalized predictive control (GPC) system based on the parametrization of two-degree-of freedom control systems. The objective is to design the GPC system which guarantees the stability of the control system for a perturbed plant. The design procedure of our proposed method consists of three steps. First, we design a basic controller for a nominal plant using the LQG method and parametrize a whole control system. Next, we identify the deviation between the perturbed plant and the nominal one using a closed-loop identification method and design a free parameter of parametrization to stabilize the closed-loop system. Finally, we design a feedforward controller so as to incorporate GPC technique into our controller structure. A numerical example is presented to show the effectiveness of our proposed method.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 1988년도 전기.전자공학 학술대회 논문집
• /
• pp.105-108
• /
• 1988

In this paper, a control system design method is proposed for DC motor drive. A state space model is used to control sysytem and for closed loop system the technique of pole assignment is applied. The control system is designed with state feedback theory and to improve the response further more feedforward theory is applied to control system. The microprocessor as a controller and the interfaces in the system are proposed. Digital simulation results for step changes in reference velocity and load torque are shown.

• 대한전기학회논문지:시스템및제어부문D
• /
• 제54권4호
• /
• pp.220-227
• /
• 2005

This paper presents hybrid artificial intelligent(HAI) controller based on the vector controlled IPMSM drive system. And it is based on artificial technologies that adaptive neural network fuzzy(A-NNF) is to speed control and artificial neural network(ANN) is to speed estimation. The salient feature of this technique is the HAI controller The hybrid action tolerates any inaccuracies in the fuzzy logic assignment rules or in the neural network stationary weights. Speed estimators using feedforward multilayer and artificial neural network(ANN) are compared. The back-propagation algorithm is easy to derived the estimated speed tracks precisely the actual motor speed. This paper presents the theoretical analysis as well as the simulation results to verify the effectiveness of the new hybrid intelligent control.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2002년도 하계학술대회 논문집 D
• /
• pp.2268-2271
• /
• 2002

NLPCA(Nonlinear principal component analysis is a novel technique for multivariate data analysis, similar to the weil-known method of principal component analysis. NLPCA operates by a feedforward neural network called AANN(AutoAssociative Neural Network) which performs the identity mapping. In this work, a sensor fault defection system based on NLPCA is presented. To verify its applicability, simulation study on the data supplied from Saemangeum measurement stations is executed.

• 제어로봇시스템학회논문지
• /
• 제18권2호
• /
• pp.87-93
• /
• 2012

In this paper, a formation guidance method for UAVs (Unmanned Aerial Vehicles) to simulate the formation flight of birds proposed. The proposed method solves all issues of approaching for formation, formation keeping, and scarce chance to be collided with each UAV during formation process. Also, we design the feedforward controller to compensate the change of speed and heading for maneuvering of the leader UAV and the feedback controller to consider the response lag of the system. The stability and performance of the proposed controller is verified via numerical simulations of the full 6-Dof model of UAV.