• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.04a
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• pp.483-487
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• 1996

We consider the estimation of the position and orientation of 6 DOF motion bed (Stewart platform) from the measured cylinder length. The solution of forward kinematics is not solved yet as a useful realtime application tool because of the complity of the equation with multiple solutiple solutions. Hence we suggest an algorithm for the estimation of forward kinematics solution using Luenberger observer withnonlinear error correction term. The Luenberger observer withlinear model shows that the estimation error does not go to zero in steadystate due to the linearization error of the dynamic model. Hence the linear observer is modified using nonlinear measurement error equation and we prove thd practical stability of the estimation error dynamics of the proposed observer using lyapunov function.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2002.09a
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• pp.213-220
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• 2002

본 연구에서는 최근에 개발된 Unscented Particle Filter (UPF)를 사용한 비선형 동적 구조계의 구조계수 규명기법이 연구되었다. 일반적인 비선형 구조계수 추정 문제의 일반 해는 존재하지 않으나, 그에 대한 대안으로써 선형 근사 기법인 extended Kalman filter (EKF)가 비선형 동적 구조계수의 추정에 주로 사용되어왔다. 그러나, EKF는 구간 선형(piecewise linear) 가정으로 인해 biased estimator이고 비선형성이 상대적으로 높을 때 오차가 큰 추정치를 주는 단점을 가진다. 이를 보완하기 위해서 UPF가 개발되었고, 이 기법은 particle filter의 일종으로써 Unscented Kalman filter (UKF)를 사용하여 importance proposal distribution을 생성한다. 수치실험이 SDOF와 MDOF에 대하여 3가지 경우에 대해서 수행되었다. 비선형 SDOF의 수치 실험으로부터 잡음이 가해진 상태에서 UKF가 EKF에 비해 초기 공분산 행렬의 가정에 대해 정확하고 강인한 추정결과를 보여줌을 보였다 최하층의 column에 비선형 거동이 발생하는 5층 전단 빌딩모형의 수치실험으로부터 UKF가 복잡한 구조물의 구조계수 추정능력이 있음을 보여주었다. 여러 가지 수치실험은 UPF가 EKF보다 비선형 동적 구조계수 추정에 있어서 더 나은 방법임을 보여 주었다.

• Proceedings of the KIEE Conference
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• 2007.04a
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• pp.14-16
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• 2007

The statistics for the Gaussian noise mixed with impulsive noise are modelled. The denoising algorithm called amplitude-limited sample average filter is derived, which is optimal in terms of minimizing mean square errors under the assumption that contaminating noise is heavy-tailed Gaussian distributed. Its performance is shown to be excellent when image is corrupted mainly with Gaussian noise. However, it shows visually grainy output as the amount of impulsive noise increases. In order to overcome such problems, it is combined with the myriad filter to propose an amplitude-limited myriad filter. Simulation shows it effectively removes both Gaussian and impulsive noise, not blurring edges severey.

• Proceedings of the KIPE Conference
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• 2003.11a
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• pp.182-185
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• 2003

This paper combines the adaption of MRAS with the ability of NN for better modeling of nonlinear systen It presents an MRAS using an NN in the adaption mechanism. The technique is applied to a IPMSM drive. The torque constant and stator resistance variations on the speed and position estimations over a wide speed range has been studied. The NN estimators are able to track the varying parameter of different speeds with consistent performance. The validity of the proposed estimator is confirmed by the operating characteristics controlled by neural networks control.

• Journal of Welding and Joining
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• v.17 no.6
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• pp.100-107
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• 1999

In this paper, we deal with the speckle noise reduction and parameter estimation of ultrasonic NDT(non-destructive test) signals obtained during weld inspection of piping. The overall approach consists of three major steps, namely, speckle noise analysis, proposition of wavelet domain AR(autoregressive) model and flaw detection by proposed model parameter. The data are first processed whereby signals obtained using vertical and angle beam transducer. Correlation properties of speckle noise are then analyzed using multiresolution analysis in wavelet domain. The parameter estimation curve obtained using the proposed model is classified a flaw in weld region where is contaminated by severe speckle noise and also clear flaw signal is obtained through CA-CFAR threshold estimator that is a nonlinear post-processing method for removing the noise from reconstructed ultrasonic signal.

• Journal of the Earthquake Engineering Society of Korea
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• v.20 no.1
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• pp.45-54
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• 2016

This study suggests a prediction model of ground motion spectral shape considering characteristics of earthquake records in Korea. Based on the Graizer and Kalkan's prediction procedure, a spectral shape model is defined as a continuous function of period in order to improve the complex problems of the conventional models. The approximate spectral shape function is then developed with parameters such as moment magnitude, fault distance, and average shear velocity of independent variables. This paper finally determines estimator coefficients of subfunctions which explain the corelation among the independent variables using the nonlinear optimization. As a result of generating the prediction model of ground motion spectral shape, the ground motion spectral shape well estimates the response spectrum of earthquake recordings in Korea.

• Proceedings of the KIEE Conference
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• 2000.07b
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• pp.1126-1129
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• 2000

The equivalent transformation of a brushless DC motor into an separately exited DC motor has been possible with the vector control technique. Vector control is an effective technique for controlling variable speed drives of brushless DC motors. Conventional vector controllers, however, suffer from electrical machine parameter variations because these controllers depend on the parameters. This paper presents the vector control of brushless DC motor using a neural network. In the proposed method, a neural network is employed as on-line estimator of the nonlinear dynamic equations of brushless DC motor. The neural network based vector controller has the advantage of robustness against machine parameter variations as compared with conventional vector controller The simulation results using Matlab/Simulink verify the useful of the proposed method.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2092-2095
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• 2001

In this paper, the performance of a new alternative form of three-axis attitude estimation algorithm for a rigid body is evaluated via simulation for the situation where the observed vectors are the estimated baselines of a GPS antenna array. This method is derived based on a simple iterative nonlinear least-squares with four elements of quaternion parameter. The representation of quaternion parameters for three-axis attitude of a rigid body is free from singularity problem. The performance of the proposed algorithm is compared with other eight existing methods, such as, Transformation Method (TM), Vector Observation Method (VOM), TRIAD algorithm, two versions of QUaternion ESTimator (QUEST), Singular Value Decomposition (SVD) method, Fast Optimal Attitude Matrix (FOAM), Slower Optimal Matrix Algorithm (SOMA).

• Journal of the Korean Data and Information Science Society
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• v.25 no.3
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• pp.677-684
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• 2014

Support vector quantile regression (SVQR) is capable of providing more complete description of the linear and nonlinear relationships among random variables. To improve the estimation performance of SVQR we propose to use SVQR ensemble with bagging (bootstrap aggregating), in which SVQRs are trained independently using the training data sets sampled randomly via a bootstrap method. Then, they are aggregated to obtain the estimator of the quantile regression function using the penalized objective function composed of check functions. Experimental results are then presented, which illustrate the performance of SVQR ensemble with bagging.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.4
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• pp.838-842
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• 2011

This paper proposes an online adaptive neuro-controller for a wind energy conversion system (WECS) that is a highly nonlinear system intrinsically. In real application, to obtain exact system parameters such as power coefficient, many measuring instruments and implementations are required, which is very difficult to perform. This shortcoming can be avoided by introducing neural network in the controller design in this paper. The proposed adaptive neural control scheme using radial-basis function network (RBFN) needs no system parameters to meet control objectives. Combining derivative estimator for wind velocity, the whole closed-loop system is shown to be stable in the sense of Lyapunov.