• Communications for Statistical Applications and Methods
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• v.13 no.3
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• pp.503-512
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• 2006

In this paper, the Inverse Weibull distribution parameters have been estimated using a new estimation technique based on the non-parametric kernel density function that introduced as an alternative and reliable technique for estimation in life testing models. This technique will require bootstrapping from a set of sample observations for constructing the density functions of pivotal quantities and thus the confidence intervals for the distribution parameters. The performances of this technique have been studied comparing to the conditional inference on the basis of the mean lengths and the covering percentage of the confidence intervals, via Monte Carlo simulations. The simulation results indicated the robustness of the proposed method that yield reasonably accurate inferences even with fewer bootstrap replications and it is easy to be used than the conditional approach. Finally, a numerical example is given to illustrate the densities and the inferential methods developed in this paper.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.14 no.3
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• pp.38-46
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• 2005

The destructive method is reliable and widely used for the estimation of material degradation but it have time-consuming and a great difficulty in preparing specimens from in-service industrial facilities. Therefore, the estimation of degraded structural materials used at high temperature by nondestructive evaluation such as electric resistance method, replica method, Barkhausen noise method, electro-chemical method and ultrasonic method are strongly desired. In this study, various nondestructive evaluation(NDE) parameters of the Barkhausen noise method, such as MPA(Maximum Peak Amplitude), RMS, IABNS(Internal Area of Barkhausen Noise on Signal) and average amplitude of frequency spectrum are investigated and correlated with thermal damage level of 2.25cr-1.0Mo steel using wavelet analysis. Those parameters tend to increase while thermal degradation proceeds. It also turns out that the wavelet technique can help to reduce experimental false call in data analysis.

• Communications for Statistical Applications and Methods
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• v.11 no.1
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• pp.79-91
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• 2004

In this thesis, Bayesian parameter estimation procedure is discussed for the mean change model of multivariate normal random variates under the assumption of noninformative priors for all the parameters. Parameters are estimated by Gibbs sampling method. In Gibbs sampler, the change point parameter is generated by Metropolis-Hastings algorithm. We apply our methodology to numerical data to examine it.

• Journal of the Korean Data and Information Science Society
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• v.7 no.2
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• pp.263-272
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• 1996

In this parer, under random censorship model, an estimation of scale and shape parameters in Weibull lifetime model is considered. Based on nonparametric estimator of survival function, the least square method is proposed. The proposed estimation method is simple and the performance of the proposed estimator is as efficient as maximum likelihood estimators. An example is presented, using field winding data. Simulation studies are performed to compare the performaces of the proposed estimator and maximum likelihood estimator.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.10a
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• pp.21-25
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• 2004

This paper suggests the techniques in determining the values of the steady-state equivalent circuit parameters of a three-phase squirrel-cage induction machine using immune algorithm. The parameter estimation procedure is based on the steady state phase current versus slip and input power versus slip characteristics. The proposed estimation algorithm is of a nonlinear kind based on clonal selection in immune algorithm. The machine parameters are obtained as the solution of a minimization of least-squares cost function by immune algorithm. Simulation shows better results than the conventional approaches.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.11
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• pp.2776-2788
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• 1996

A new feature-based motion parameter estimation for arbitrary-shaped regions is proposed. Existing motion parameter estimation algorithms such as gradient-based algorithm require iterations that are very sensitive to initial values and which often converge to a local minimum. In this paper, the motion parameters of an object are obtained by solving a set of linear equations derived by the motion of salient feature points of the object. In order to estimate the displacement of the feature points, a new process called the "bi-directional correspondence scheme" is proposed to ensure the robjstness of correspondence. The proposed correspondence scheme iteratively selects the feature points and their corresponding points until unique one-to-one correspondence is established. Furthermore, initially obtained motion paramerters are refined using an iterative method to give a better performance. The proposed algorithm can be used for motion estimationin object-based image coder, and the experimental resuls show that the proposed method outperforms existing schemes schemes in estimating motion parameters of objects in image sequences.sequences.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.110-110
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• 2001

By the state transformation including independent outputs functions, a nonlinear process model can be decomposed into two subsystems; the one(design model) is described in output variables as new states and used for control system synthesis and the other(disturbance model) is described in the original unavailable states and its couplings with the design model are treated as uncertain time-varying parameters in the design model. Its existence with respect to the design model is ignored. So, the design model is and uncertain time-variant system. Control synthesis based on a reduced design model is a combined form of a time-variant input-output linearization with parameter estimation. The parameter estimation is also based on the design model and it gives the parameter estimates such that the estimated outputs follow the actual outputs in a specified way. The disturbances form disturbance model and as well all the other uncertainties affecting the outputs will be reflected into the estimated parameters used in the linearizing control law.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.425-430
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• 1991

For satellite orbit determination, a satellite (K-3H) which is affected by the earth's gravitational field and the earth's atmospheric drag, the sun, and the moon is chosen as a dynamic model. The state vector include orbit parameters, uncertain parameters associated with perturbations and tracking stations. These perturbations include gravitational constant, atmospheric drag, and jonal harmonics due to the earth nonsphericity. Early orbit was obtained with given the predicted orbital parameter of the satellite. And orbit determination, which is applied to Extended Kalman Filter(EKF) for real time implementation , use the observation data which is given by satellite tracking radar system and then orbit estimation is accomplished. As a result, extended sequential estimation algorithm has a fast convergence and also indicate effectiveness for real time operation.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.7
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• pp.644-649
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• 2006

In this paper, a methodology for estimating the model parameters of a discrete-time system and tuning a digital PID controller based on the estimated model and a genetic algorithm is presented. To deal with optimization problems regarding parameter estimation and controller tuning, pseudo-parallel genetic algorithms(PPGAs) are used. The parameters of a discrete-time system are estimated using both the model adjustment technique and a PPGA. The digital PID controller is described by the pulse transfer function and then its three gains are tuned based on both the model reference technique and another PPGA. A set of experimental works on two processes are carried out to illustrate the performance of the proposed method.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.362-366
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• 1990

The resistance spot welding process has been extensively used for joining of sheet metals, which are subject to variation of many process variables. Many qualitative analyses of sampled process variables have been successfully attempted to achieve a uniform nugget size. In this paper, the electrode movement signal which is a good indicative of the nugget size was examined by introducing a mathematical model with four parameters. A neural network method was applied for the estimation of the nugget size by four parameters. The prediction by the neural network is in good agreement with the actual nugget size. The results are quite promising in that the qualitative estimation of the invisible nugget size can be achieved without destructive testing of the welds.