• 한국광학회지
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• 제10권3호
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• pp.233-236
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• 1999

광섬유 브래그 격자(fiber Bragg grating)와 서큘레이터(circulator)를 이용하여 좁은 선폭의 단일 종모드로 발진하는 단방향 어븀첨가 광섬유 레이저를 간결하게 구성하엿다. 이 레이저에서 발진되는 단일 종 모드의 선폭은 마하젠더 간섭계를 이용한 delayed self-heterodyne 검출 방법을 사용하여 측정하였고, 측정된 선폭은 5 kHz 였다. 단일 종모드로 발진되는 최대 광출력은 2.7 mW였으며, 발진 중심파장은 1548 nm였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 춘계학술대회 논문집 전기기기 및 에너지변환시스템부문
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• pp.244-246
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• 2001

In a full digital implementation of a current regulator, the voltage output is inevitably delayed due to arithmetic calculation and PWM. In case of the synchronous frame current regulator, the time delay is accompanied by the rotation of frame. In some applications in which the ratio of sampling frequency to output frequency is not high enough, such as high power drive or super high-speed drive, it is known that the effect of rotation of frame during the delay time causes phase and magnitude error in the voltage output. The error degrades the dynamic performance and can bring about the instability of current regulator at high speed. It is also intuitively known that advancing the phase of voltage output can mitigate the instability. In this paper, the instability problems are studied analytically and a compensation method for the error has been proposed. By means of computer simulation and complex root locus analysis, comparative study with conventional methods is carried out and the effectiveness of proposed method is verified.

• 한국지능시스템학회논문지
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• 제13권3호
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• pp.334-341
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• 2003

본 논문에서는 신경망제어기의 출력을 보상하는 퍼지보상기를 갖는 리커런트 시간 지연 신경망(RTDNN) 제어기를 제안하였다. 학습이 완료된 신경망제어기를 사용하더라도 예상치 못한 외란으로 인해 플랜트의 출력이 좋지 못한 경우가 있는데, 이것을 적절하게 조절해 주기 위해 퍼지보상기를 사용하여 원하는 결과를 얻을 수 있도록 하였다. 그리고 플랜트의 역모델 신경망을 학습시킨 결과를 이용하여 주 신경망의 가중치를 변경시킴으로서 원하는 플랜트의 동적 특성을 얻게 된다. 2차 플랜트를 통한 모의실험 결과가 시간 지연 신경망(TDNN)제어기보다 더 좋은 응답 특성을 가짐을 확인할 수 있다. 제안한 제어기의 성능을 확인하기 위해 유압 서보시스템을 대상으로 DSP 프로세서를 사용하여 구현한 후 실험결과를 통하여 제안된 방법의 유용성을 보였다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 하계종합학술대회 논문집 V
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• pp.2807-2810
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• 2003

In this paper, we propose a fuzzy control method for improving the control performance by automatically tuning the scaling factor. The proposed method is that automatically tune the input scaling factor and the output scaling factor of fuzzy logic system through neural network. Used neural network is ADALINE (ADAptive Linear NEron) neural network with delayed input. ADALINE neural network has simple construct, superior learning capacity and small computation time. In order to verify the effectiveness of the proposed control method, we performed simulation. The results showed that the proposed control method improves considerably on the environment of the disturbance.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.149.5-149
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• 2001

The robot soccer simulation game is a dynamic multi-agent environment. In this paper we suggest a new reinforcement learning approach to each agent´s dynamic positioning in such dynamic environment. Reinforcement learning is the machine learning in which an agent learns from indirect, delayed reward an optimal policy to choose sequences of actions that produce the greatest cumulative reward. Therefore the reinforcement learning is different from supervised learning in the sense that there is no presentation of input-output pairs as training examples. Furthermore, model-free reinforcement learning algorithms like Q-learning do not require defining or learning any models of the surrounding environment. Nevertheless ...

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1992년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 19-21 Oct. 1992
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• pp.548-553
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• 1992

In this paper and recursive version of orthogonal ARMA identification algorithm is proposed. The basic algorithm is based on Gram-Schmidt orthogonalization of automatically selected basis functions from specified function space, but does not require explicit creation of orthogonal functions. By using two dimensional autocorrelations and crosscorrelations of input and output with constant data length, identification algorithm is extended to cope slowly time-varying or order-varying delayed system.

• 전자공학회논문지B
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• 제31B권4호
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• pp.48-54
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• 1994

A recursive on-line algorithm with orthogonal ARMA identification is proposed for linear systems with unkonwn time delay, order, and parameters. The algorithm is based on the Gram-Schmidt orthogonalization of basis functions, and extendedto recursive form by using two dimensional autocorrelations and crosscorrelations of input and output with constant data length. The proposed algorith can cope with slowly time-varying or order-varying delayed system. Various simulations reveal the performance of the algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1987년도 한국자동제어학술회의논문집; 한국과학기술대학, 충남; 16-17 Oct. 1987
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• pp.135-138
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• 1987

A method for designing a robust tracking controller for linear discrete systems is investigated. Only the observable variables are to be used in the controller synthesis. To insure the robustness, the system is augmented by a compensator at the output side. Then a feedback controller is designed using delayed values of the observable variables for the augmented system. The delay times are chosen to minimize the effect of measurement accuracy and/or noise.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 Proceedings of the Korea Automatic Control Conference, 11th (KACC); Pohang, Korea; 24-26 Oct. 1996
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• pp.48-51
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• 1996

This note considers the $H^{\infty}$ controller design problem for linear systems with time-varying delays in states. We obtain sufficient conditions for the existence of k-th order $H^{\infty}$ controllers in terms of three linear matrix ineualities(LMIs). These sufficient conditions are dependent on the maximum value of the time derivative of time-varying delay. Furthermore, we briefly explain how to construct such controllers from the positive definite solutions of their LMIs and give an example.e.

• 전자공학회논문지SC
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• 제39권2호
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• pp.140-145
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• 2002

본 논문은 단락회로 전류를 없애기 위한 CMOS 버퍼회로에 대한 것이다. 최종 구동소자는 풀-업 PMOS와 풀-다운 NMOS로 구성하고 이를 구동하기 위해 두가지 경로를 입력신호로 선택되도록 하였다. 이러한 기법으로 최종 구동회로가 짧은 시간동안 tri-state가 되어 단락회로 전류를 차단하였다. 모의 실험결과 전원전압 3.3V에서 전력-지연 곱을 기존의 Tapered 버퍼[1]와 비교하여 약 42% 줄일 수 있었다