• Earthquakes and Structures
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• v.6 no.4
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• pp.393-408
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• 2014

In this paper, a direct identification of modal parameters using the continuous wavelet transform is proposed. The purpose of this method is to transform the differential equations of motion into a system of algebraic linear equations whose unknown coefficients are modal parameters. The efficiency of the present method is confirmed by numerical data, without and with noise contamination, simulated from a discrete forced system with four degrees-of-freedom (4DOF) proportionally damped.

• Journal of international Conference on Electrical Machines and Systems
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• v.2 no.1
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• pp.69-76
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• 2013

This paper is concerned with speed tracking control problem for permanent-magnet synchronous drives (PMSM) in the presence of an variable load torque and unknown model parameters. The disturbance of speed control caused by inaccuracy of model parameters has been investigated. A load torque observer has been proposed to observe the load torque and estimate the disturbance caused by inaccuracy of model parameters. Both inertia and friction coefficient are identified in gradient descent approach. The stability condition of the observer has also been studied. Furthermore an improved feed-forward control has been introduced to reduce the speed track error. The proposed control strategy has been verified by both simulation and experimental results.

• Journal of Korean Society for Quality Management
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• v.25 no.1
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• pp.69-79
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• 1997

We consider the semiparametric linear errors-in-variables model: yi=(${\alpha}+{\beta}ui+{\varepsilon}i$, xi=ui＋${\varepsilon}i$ i=1, …, n where (xi, yi) stands for an observation vector, (ui) denotes a set of incidental nuisance parameters, (${\alpha}$ , ${\beta}$) is a vector of regression parameters and (${\varepsilon}i$, ${\delta}i$) are mutually uncorrelated measurement errors with zero mean and finite variances but otherwise unknown distributions. On the basis of a simple small-sample low-noise a, pp.oximation, we propose a new method of comparing the mean squared errors(MSE) of the various competing estimators of the true regression parameters ((${\alpha}$ , ${\beta}$). Then we show that a class of estimators including the classical least squares estimator and the maximum likelihood estimator are consistent and first-order efficient within the class of all regular consistent estimators irrespective of type of measurement errors.

• Communications for Statistical Applications and Methods
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• v.24 no.1
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• pp.15-27
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• 2017

This paper introduces a two-parameter discrete distribution based on a continuous two-parameter bathtub distribution. It is the only two-parameter discrete distribution that shows a bathtub-shaped hazard function. Some statistical properties of the distribution are discussed. Three different methods are used to estimate its two unknown parameters. The point estimators of the parameters have no closed form. The bootstrap method is used to estimate the distributions of these point estimators. Different approximations of the interval estimations for the two-parameters are discussed. Real data sets are analyzed to show how this distribution works in practice. A simulation study is performed to investigate the properties of the estimations obtained and compare their performances.

• 전기의세계
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• v.18 no.1
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• pp.15-23
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• 1969

A lot of recent researches have shown that a Pseudo Random Binary Signal is a quite effective test signal to measure the impulse response of a plant. Generally speaking, however, such a response itself is not satisfactory to determine the appropriate control parameters or control inputs. Here, the author intends to estimate the unknown parameters of the First Order Plant with Dead Time by means of correlation method using M-sequence signal. The time constant T and the dead time L of the plant are eatimated with one tracking loop by automatically adjusting delay time .tau. of M-sequence signal according to variations of T and L. In this paper, a three level M-sequence signal is used as a test signal in order to avoid troublesome operations to calculate partial derivatives of a given performance index with respect to the parameters which are usually required in the Model Method. Several experiments with analogue computer using low pass filters as averaging circuits showed good results as expected.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1997.04a
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• pp.97-105
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• 1997

A methodology to identify a baseline modal model of a complicated 3-D structure using limited structural and modal information is experimentally examined. In the first part, a system's identification theory for the methodology to identify, baseline modal responses of the structure is outlined. Next, an algorithm is designed to build a generic finite element model of the baseline structure and to calibrate the model by using only a set of post-damage modal parameters. In the second part, the feasibility of the methodology is examined experimentally using a field-tested truss bridge far which only post-damaged modal responses were measured for a few vibration modes. For the complex 3-D bridge with many members, we analyzed to identify unknown stiffness parameters of the structure by using modal parameters of the initial two modes of vibration.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.2
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• pp.65-75
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• 2004

This paper presents a generalized predictive control method based on a fuzzy neural network(FNN) model, which uses the on-line multi-step prediction, fur the intelligent control of chaotic nonlinear systems whose mathematical models are unknown. In our design method, the parameters of both predictor and controller are tuned by a simple gradient descent scheme, and the weight parameters of FNN are determined adaptively during the operation of the system. In order to design a generalized predictive controller effectively, this paper describes computing procedure for each of the two important parameters. Also, we introduce a projection matrix to determine the control input, which deceases the control performance function very rapidly. Finally, in order to evaluate the performance of our controller, the proposed method is applied to the Doffing and Henon systems, which are two representative continuous-time and discrete-time chaotic nonlinear systems, res reactively.

• Journal of the Korean Society of Industry Convergence
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• v.22 no.5
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• pp.575-582
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• 2019

Advanced driving assist system can support safety of driver and passengers which may require vehicle dynamics states as well as road geometry. It is essential to have in real-time estimation of related variables and parameters. Among the road geometry parameters, road slope angle which can not be measured is essential parameter in pose estimation, adaptive cruise control and others on sag road. In this paper, Kalman filter based method for the estimation of the vehicle dynamics and road slope angle using a nonlinear vehicle model is proposed. It uses a combination of Kalman filter as Cascade Extended Kalman Filter. CEKF uses measured vehicle states such as yaw rate, longitudinal/lateral acceleration and velocity. Unknown vehicle parameters such as center of gravity and inertia are obtained by 2 D.O.F lateral model and experimentally. Simulation and Experimental tests conducted with commercialized vehicle dynamics model and real-car.

• Journal of Korean Society for Atmospheric Environment
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• v.22 no.1
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• pp.135-143
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• 2006

The objective of this study is to develop PSCF (potential source contribution function) program and determine the optimal values of control parameters to enhance the prediction of PSCF modeling. This study provides an important information and methodologies that can be used to get better results of locating influencing sources, especially unknown and fugitive sources. To determine proper values of control parameters in PSCF model, the diagnostic assessment on the results obtained by the various input conditions was carried out. PSCF model has created and improved from version 1.0 to version 7.0 since 200 I and the measured data (at least > 100) of receptor, and the values of control input parameters should be arranged and determined to obtain reliable results in PSCF modeling. The size of modeling domain must be determined to include enough trajectories to get reliable results. And the size of grid is recommended to be 2.5 $\sim$ 5 degrees for global scale, 0.2 $\sim$ 1 degrees for regional scale and 0.05 degree for local scale.

• Journal of the Korean Data and Information Science Society
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• v.20 no.1
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• pp.39-48
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• 2009

Fisher information matrix plays an important role in statistical inference of unknown parameters. Especially, it is used in objective Bayesian inference which derives to the posterior distribution using a noninformative prior distribution and is an example of metric functions in geometry. The more parameters for estimating in a distribution are, the more complicate derivation of the Fisher information matrix for the distribution is. In this paper, we derive to the Fisher information matrix for 3-parameters Weibull distribution which is used in reliability theory using Mathematica programs.