• 한국산학기술학회논문지
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• 제15권6호
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• pp.3435-3441
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• 2014

차량의 시트 재질로 사용되는 폴리우레탄 폼의 모델링 및 동특성 예측은 승객의 승차감 향상을 위해 매우 중요하다. 본 연구에서는 폴리우레탄 폼의 정특성 시험을 통해 비선형 강성과 점탄성 특성의 시간변수 파라미터를 구하였다. 다항식과 컨벌루션 적분법을 이용하여 폴리우레탄 폼의 비선형 특성과 점탄성 특성을 수학적으로 모델링하였다. 이와 같은 비선형 진동 모델에 대하여 수치적분 방법을 이용하여 시트 바닥 변위에 대한 진동 응답을 계산하였다. 폴리우레탄 폼의 비선형 특성과 점탄성 특성이 진동계에서 미치는 영향을 단순 1자유도계와 인체모델을 이용하여 분석하였다. 결과적으로 폴리우레탄 폼의 점탄성 특성이 승차감을 위한 설계파라미터로 고려되어야 하는 것으로 나타났다.

• 한국정보처리학회논문지
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• 제7권5호
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• pp.1361-1369
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• 2000

For the implementation of 3D Sound Localization system, the binaural filtering by HRTFs is generally employed. But the HRTF filter is of high order and its coefficients for all directions have to be stored, which imposes a rather large memory requirement. To cope with this, research works have centered on obtaining low dimensional HRTF representations without significant loss of information and synthesizing the original HRTF efficiently, by means of feature extraction methods for multivariate dat including PCA. In these researches, conventional linear PCA was applied to the frequency domain HRTF data and using relatively small number of principal components the original HRTFs could be synthesized in approximation. In this paper we applied neural network based nonlinear PCA model (NLPCA) and the nonlinear PLS repression model (NLPLS) for this low dimensional HRTF modeling and analyze the results in comparison with the PCA. The NLPCA that performs projection of data onto the nonlinear surfaces showed the capability of more efficient HRTF feature extraction than linear PCA and the NLPLS regression model that incorporates the direction information in feature extraction yielded more stable results in synthesizing general HRTFs not included in the model training.

• 한국산학기술학회논문지
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• 제12권11호
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• pp.5194-5201
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• 2011

본 논문에서는 부분적으로 알고 있는 비선형 마찰력이 있는 2차 기계시스템에 대한 강인한 적응제어 기법을 제시한다. 제시하는 적응제어 방법은 잡음이나 모델링 에러가 없는 경우에는 위치 및 속도 추적 오차는 점근적으로 영으로 수렴함을 보장하며, 잡음이나 모델링 에러가 있는 경우에는 그동안 발표된 다른 논문의 결과와 달리 디자인 변수를 적절하게 선택하면 위치 및 속도 추적 오차를 원하는 범위까지 줄일 수 있음을 보였으며 이는 시뮬레이션 결과를 통해 입증하였다.

• 전기전자학회논문지
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• 제13권1호
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• pp.30-35
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• 2009

입력 상수에 불확실성이 있는 최소위상 비선형시스템에 대해 경로추적 오차가 없는 제어기 설계를 제안하였다. 모델링 불확실성이 존재하는 비선형 시스템에 대해 상태 변수를 예측하기 위해 고 이득 관측기를 사용하였다. 적분형 가변 구조 제어 방식을 이용하여 점근적으로 경로추적오차가 0 되는 새로운 제어기를 제안하여 기존의 경로 추적오차를 감소시켰다. 제어기 설계의 정당성은 폐루프 시스템을 Lyapunov 분석 방법을 통해 보였다. 또한 제어기의 성능을 모의시험을 통해 보였다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.646-651
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• 2004

This paper deals with a new adaptive fuzzy sliding mode controller and its application to an inverted pendulum. We propose new method of adaptive fuzzy sliding mode control scheme that the fuzzy logic system is used to approximate the unknown system functions in designing the SMC of uncertain nonlinear systems. The controller's construction and its analysis involve sliding modes. The proposed controller consists of two components. Sliding mode component is employed to eliminate the effects of disturbances, while a fuzzy model component equipped with an adaptation mechanism reduces modeling uncertainties by approximating model uncertainties. To demonstrate its performance, the proposed control algorithm is applied to an inverted pendulum. The results show that both alleviation of chattering and performance are achieved

• The Journal of the Acoustical Society of Korea
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• 제15권2E호
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• pp.50-59
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• 1996

In this paper, an adaptive precompensator, which can reduce the distortion of a Wiener system effectively, is proposed. The previous techniques for adaptive precompensation, based on the Volterra series modeling to compensate the distortion of a nonlinear system, are not suitable for real-time implementation due to high computational burden and slow convergence burden and slow convergence rate. This paper presents an adaptive precompensation technique for the class of nonlinear subsystem, referred to as Wiener system. An adaptive algorithm for adjusting the parameters of a precompensator, structured by a hammerstein model, is derived using the stochastic gradient method. Also, an adaptive precompensatin technique which can effectively reduce nonlinear distortion in μ-law type of saturation characteristics is proposed. The validity of the proposed algorithm is confirmed through simulation by applying it to known Wiener systmes and a typical loudspeaker model.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제11권1호
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• pp.12-18
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• 2011

This paper deals with a new adaptive fuzzy sliding mode controller and its application to an inverted pendulum. We propose a new method of adaptive fuzzy sliding mode control scheme that the fuzzy logic system is used to approximate the unknown system functions in designing the SMC of uncertain nonlinear systems. The controller's construction and its analysis involve sliding modes. The proposed controller consists of two components. Sliding mode component is employed to eliminate the effects of disturbances, while a fuzzy model component equipped with an adaptation mechanism reduces modeling uncertainties by approximating model uncertainties. To demonstrate its performance, the proposed control algorithm is applied to an inverted pendulum. The results show that both alleviation of chattering and performance are achieved.

• 한국농공학회논문집
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• 제49권2호
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• pp.27-36
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• 2007

This study, the present study wishes to develop a model that estimates shear strength characteristics of steel fiber reinforced concrete using artifical neural network models. Neural network models, developed as mathematical models, are being widely used not only in its original purpose of pattern recognition, but also in application fields by the function's nonlinear loaming and interpolar ability Neural network has a repetitive rotation algorithm that can cyclically and repeatedly estimate system conditions and parameter ideal values, and it can be used in the modeling of the nonlinear system by nonlinear characteristic functions that construct the system.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1997년도 춘계학술대회 학술발표 논문집
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• pp.267-270
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• 1997

In this paper, we proposed an optimazation method using Genetic Algorithm for nonlinear system modeling. Fuzzy Neural Network(FNNs) was used as basic model of nonlinear system. FNNs was fused of Fuzzy Inference which has linguistic property and Neural Network which has learning ability and high tolerence level. This paper, We used FNNs which was proposed by Yamakawa. The FNNs was composed Simple Inference and Error Back Propagation Algorithm. To obtain optimal model, parameter of membership function, learning rate and momentum coefficient of FNNs are tuned using genetic algorithm. And we used simplex algorithm additionaly to overcome limit of genetic algorithm. For the purpose of evaluation of proposed method, we applied proposed method to traffic choice process and waste water treatment process, and then obtained more precise model than other previous optimization methods and objective model.

• 한국통신학회논문지
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• 제22권4호
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• pp.696-705
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• 1997

This paper presents an efficient adaptive predistortion technique compensating linear and nonlinear distortions caused by high-power amplifier (HPA) with memory in OFDM systems. The efficient adaptive data predistortion techniques proposed for compensation of HPA with memory in single carrier systems cannot be applied to OFDM systems since the possible input levels for HPA is infinite in OFDM systems. Also, previous adaptive predistortion techniques, based on Volterra series modeling, are not suitable for real-time implementation due to high computational burden and slow convergence rate. In the proposed approach, the memoryless HPA preceded by a linear filter in OFDM systems is modeled by the Wiener system which is then precompensated by the proposed adaptive predistorter with a minimum number of filter taps. An adaptive algorithm for adjusting the proposed adaptive predistorter is derived using the stochastic gradient method. It is demonstrated by computer simulation that the performance of OFDM system suffering from nonlinear distortion can be greatly improved by the proposed efficient adaptive predistorter using a small number of filter taps.