• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.5
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• pp.525-530
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• 2006

This paper presents an efficient input variable selection method using both fixed-point independent component analysis(FP-ICA) and adaptive partition mutual information(AP-MI) estimation. FP-ICA which is based on secant method, is applied to quickly find the independence between input variables. AP-MI estimation is also applied to estimate an accurate dependence information by equally partitioning the samples of input variable for calculating the probability density function(PDF). The proposed method has been applied to 2 problems for selecting the input variables, which are the 7 artificial signals of 500 samples and the 24 environmental pollution signals of 55 samples, respectively The experimental results show that the proposed methods has a fast and accurate selection performance. The proposed method has also respectively better performance than AP-MI estimation without the FP-ICA and regular partition MI estimation.

• Proceedings of the IEEK Conference
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• 2000.11d
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• pp.9-12
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• 2000

This paper proposes an improved image illumination estimation method based on the conventional color constancy algorithm. The most important process of color constancy algorithm is the estimation of the spectral distributions of illuminant of an input image. To estimate of the spectral distributions of illuminant of an input image, we use the brightest pixel values and the values of surface reflectance of an input image using a principal component analysis of the given munsell chips. We estimate a CIE tristimulus values of an input image using the estimated .spectral distribution of illuminant and recover an image by scaling it regularity. From the experimental results, the proposed method was effective in estimating the image illumination

• Journal of the Korea Convergence Society
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• v.4 no.4
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• pp.41-48
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• 2013

Observer design for system with unknown input was carried out. First, Kalman filter was considered to estimate system state with White noise. With the results of Kalman filter design, state observer, controller properties, including controllability and observability, and the Kalman filter structure and algorithm were also studied. Kalman filter algorithm was applied to Position and velocity measurement based on Kalman filter with white noise, and it was constructed and achieved by programming based on Matlab programming. Finally, observer for system with unknown input was constructed with the help of Gershgorin's disc theorem. With the designed observer, system states was constructed and applied to system with unknown input. By simulation results, estimation performance was verified. In this project, state feedback control theory, observer theory and relevant design procedure, as well as Kalman filter design were understood and used in practical application.

• Smart Structures and Systems
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• v.10 no.1
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• pp.15-31
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• 2012

This study proposes an active control method to suppress beam-rotating machinery system vibrations. The present control method is a combination of the fuzzy input estimation method (FIEM) and linear quadratic Gaussian problem (LQG) algorithms. The FIEM can estimate the unknown input and optimal states by measuring the dynamic displacement, the optimal estimated states into the feedback control; thereby obtaining the optimal control force for a random linear system. Active vibration control of a beam-rotating machinery system is performed to verify the feasibility and effectiveness of the proposed algorithm. The simulation results demonstrate that the proposed method can suppress vibrations in a beam-machine system more efficiently than the conventional LQG method.

• Structural Engineering and Mechanics
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• v.37 no.4
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• pp.351-365
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• 2011

This study proposes an inverse estimation method for the input forces of a fixed beam structural system. The estimator includes the fuzzy Kalman Filter (FKF) technology and the fuzzy weighted recursive least square method (FWRLSM). In the estimation method, the effective estimator are accelerated and weighted by the fuzzy accelerating and weighting factors proposed based on the fuzzy logic inference system. By directly synthesizing the robust filter technology with the estimator, this study presents an efficient robust forgetting zone, which is capable of providing a reasonable trade-off between the tracking capability and the flexibility against noises. The period input of the fixed beam structure system can be effectively estimated by using this method to promote the reliability of the dynamic performance analysis. The simulation results are compared by alternating between the constant and adaptive and fuzzy weighting factors. The results demonstrate that the application of the presented method to the fixed beam structure system is successful.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.5
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• pp.52-61
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• 1993

This paper deals with adaptive control method of a robot manipulator with one-flexible link. ARMA model is used as a prediction and estimation model, and adaptive control scheme consists of parameter estimation part and adaptive controller. Parameter estimation part estimates ARMA model's coefficients by using recursive least-squares(RLS) algorithm and generates the predicted output. Variable forgetting factor (VFF) is introduced to achieve an efficient estimation, and adaptive controller consists of reference model, error dynamics model and minimum prediction error controller. An optimal input is obtained by minimizing input torque, it's successive input change and the error between the predicted output and the reference output.

• Proceedings of the KSR Conference
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• 2009.05b
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• pp.259-264
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• 2009

This paper proposes a real-time estimation of the boost inductance in a single-phase AC/DC parallel PWM converter for high-speed EMU. The estimation procedure of the boost inductance is only based on the variation of input current and the input AC voltage measurement. The estimated boost inductance is optimized by the least square method. This estimation technique can improve the performance of current controller and reduce the harmonics of the input current in the feed-forward controller. The validity of proposed technique is verified through the MATLAB SIMULINK simulation results.

• Journal of the Society of Naval Architects of Korea
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• v.43 no.5 s.149
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• pp.597-603
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• 2006

The estimations of distributed and joint-excited input power for Power Flow Analysis are accomplished in this paper. Using Fourier transform, the displacements of infinite structures are derived, and the input power of distributed excitation can be estimated. The obtained results compare the real input power with the estimation of input power. When the exciting force acts on the joint of coupled structures, it is estimating the power that is transferred to each structure. Appling this input power, the results of energy density and intensity of Power Flow Analysis can be compared with the classical solutions.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.5
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• pp.607-614
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• 1999

This paper deals with the design problem of the state estimator for servo system. The servo system has input time delay which depends on the computational time of control algorithm. The delayed input is a factor that brings out the state estimation error. So in order to reduce the state estimation error of the system, we propose a state estimator in which the delayed input of the system is considered. For this purpose, discrete time state space model is established accounting for the delayed input and a state estimator is designed based on this model. Kalman filter algorithm is employed in the design of the state estimator. The proposed estimator is used in the speed control of servo system with delayed input. Performance of the proposed state estimator is exemplified via simulations and experiments for servo system. Also, robustness of the proposed estimator to modeling error by variation of the system parameters is also shown in simulations.

• Wind and Structures
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• v.17 no.4
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• pp.451-464
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• 2013

This study develops an extended inverse input estimation algorithm with intelligent adaptive fuzzy weighting to effectively estimate the unknown input wind load of nonlinear structural systems. This algorithm combines the extended Kalman filter and recursive least squares estimator with intelligent adaptive fuzzy weighting. This study investigated the unknown input wind load applied on a tower structural system. Nonlinear characteristics will exist in various structural systems. The nonlinear characteristics are particularly more obvious when applying larger input wind load. Numerical simulation cases involving different input wind load types are studied in this paper. The simulation results verify the nonlinear characteristics of the structural system. This algorithm is effective in estimating unknown input wind loads.