• Proceedings of the KIEE Conference
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• 2003.10a
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• pp.279-282
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• 2003

In this paper, we propose a new high resolution frequency estimator for real sinusoids by using short time data and the AWLS/MFT (Adaptive Weighted Least Squares/ Modulation Function Technique) algorithm. Monte-Carlo simulations verify better performances of the proposed frequency estimator and demonstrate that the proposed AWLS sinusoidal estimator is a high resolution estimator.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10b
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• pp.753-758
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• 1988

In this paper, we propose a new method of designing a reduced-order controller for a linear discrete-time system. Firstly, we study a design problem for a two-degree-of-freedom control system with a feedforward controller. Secondly, in order to obtain a reduced-order controller, frequency-weighted least squares approximation problems are considered. Thirdly, we propose a synthesis procedure of a reduced-order controller. Finally, an example is given to illustrate the effectiveness of this proposed method.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.6
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• pp.780-785
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• 1998

In this paper, it is shown that the conventional methods for dealing with the singularity problem of a manipulator can be generalized as a local minimization problem with differently weighted objective functions. A new damping method proposed in this article automatically determines the damping amounts for singular values, which are inversely proportional to the magnitude of the singular values. Furthermore, this can be done without explicitly computing the singular values. The proposed method can be applied to all the manipulators with revolute joints.

• Journal of the Korean Institute of Telematics and Electronics
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• v.24 no.4
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• pp.597-602
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• 1987

In this paper, an adaptive observe based upon the exponentially weighted least-squares method is implemented in the design of a model reference adaptive controller for an unknown time-invariant discrete single-input single-output linear plant. A method of selecting the state variable filter is proposed. In this scheme, all the past data are weithted exponentially with the weighting coefficient.

• Journal of the Korean Statistical Society
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• v.24 no.2
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• pp.349-360
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• 1995

This paper deals with the robustness properties of the minimum disparity estimation in linear regression models. The estimators defined as statistical quantities whcih minimize the blended weight Hellinger distance between a weighted kernel density estimator of the residuals and a smoothed model density of the residuals. It is shown that if the weights of the density estimator are appropriately chosen, the estimates of the regression parameters are robust.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.323-328
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• 1998

In this paper, a new input estimation algorithm is proposed for target tracking problem. The unknown target maneuver is approximated by a linear combination of independent time functions and the coefficients are estimated by using a weighted least-squares estimation technique. The proposed algorithm is verified by computer simulation of a realistic two-dimensional tracking problem. The proposed algorithm provides significant improvements in estimation performance over the conventional input estimation techniques based on the constant-input assumption.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.40 no.2
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• pp.183-191
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• 1991

This paper develops a pole-zero assignment self-tuning regulator utilizing the method of a neural network in the plant parameter estimation. An approach to parameter estimation of the plant with a Hopfield neural network model is proposed, and the control characteristics of the plant are evaluated by means of a simulation for a second-order linear time invariant plant. The results obtained with those of Exponentially Weighted Recursive Least Squares(EWRLS) method are also shown.

• Geophysics and Geophysical Exploration
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• v.8 no.2
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• pp.129-136
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• 2005

The damped least-squares inversion has become a most popular method in finding the solution in geophysical problems. Generally, the least-squares inversion is to minimize the object function which consists of data misfits and model constraints. Although both the data misfit and the model constraint take an important part in the least-squares inversion, most of the studies are concentrated on what kind of model constraint is imposed and how to select an optimum regularization parameter. Despite that each datum is recommended to be weighted according to its uncertainty or error in the data acquisition, the uncertainty is usually not available. Thus, the data weighting matrix is inevitably regarded as the identity matrix in the inversion. We present a new inversion scheme, in which the data weighting matrix is automatically obtained from the analysis of the data resolution matrix and its spread function. This approach, named 'extended active constraint balancing (EACB)', assigns a great weighting on the datum having a high resolution and vice versa. We demonstrate that by applying EACB to a two-dimensional resistivity tomography problem, the EACB approach helps to enhance both the resolution and the stability of the inversion process.

• Smart Structures and Systems
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• v.22 no.3
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• pp.315-326
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• 2018

This paper presents a novel time-domain method for the identification of plastic rotations and stiffness parameters of single-bay frames with nonlinear plastic hinges. Each plastic hinge is modelled as a pseudo-semi-rigid connection with nonlinear hysteretic moment-curvature characteristics at an element end. Through the comparison of the identified end rotations of members that are connected together, the plastic rotation that furnishes information of the locations and plasticity degrees of plastic hinges can be identified. The force consideration of the frame members may be used to relate the stiffness parameters to the elastic rotations and the excitation. The damped-least-squares method and damped-and-weighted-least-squares method are adopted to estimate the stiffness parameters of frames. A noise-removal strategy employing a de-noising technique based on wavelet packets with a smoothing process is used to filter out the noise for the parameter estimation. The numerical examples show that the proposed method can identify the plastic rotations and the stiffness parameters using measurements with reasonable level of noise. The unknown excitation can also be estimated with acceptable accuracy. The advantages and disadvantages of both parameter estimation methods are discussed.

• Transactions of the Korean Society of Mechanical Engineers
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• v.12 no.4
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• pp.694-704
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• 1988

A recursive parameter estimation scheme utilizing the variance perturbation method is applied to the workpiece deflection model during CNC turning process, in order to improve the cylindricity of slender workpiece. It features that it is based on exponentially weighted recursive least squares method with post-process measurement of finish surfaces at two locations and it does not require a priori knowledge on the time varying deflection model parameter. The measurements of finish surfaces by using two proximity sensors mounted face to face enable one to identify the straightness, guide-way, run-out eccentricity errors. Preliminary cutting tests show that the straightness error of the finish surface due to workpiece deflection during cutting is most dominant. Identifying the errors and recursive updating the parameter, the off-line control is carried out to compensate the workpiece deflection error, through single pass cutting. Experimental results show that the proposed method is superior to the conventional multi-pass cutting and the direct compensation control in cutting accuracy and efficiency.