• International Journal of Reliability and Applications
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• v.16 no.2
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• pp.55-79
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• 2015

It is well known that using any additional information in the estimation of unknown parameters with new sample of observations diminishes the sampling units needed and minimizes the risk of new estimators. There are many rational reasons to assure that the existence of additional information in practice and there exists many practical cases in which additional information is available in the form of target value (initial value) about the unknown parameters. This article is described the problem of how the prior initial value about the unknown parameters can be utilized and combined with classical Bayes estimator to get a new combination of Bayes estimator and prior value to improve the properties of the new combination. In this article, two classes of Bayes-shrinkage and preliminary test Bayes-shrinkage estimators are proposed for the scale parameter of exponential distribution. The bias, risk and risk ratio expressions are derived and studied. The performance of the proposed classes of estimators is studied for different choices of constants engaged in the estimators. The comparisons, conclusions and recommendations are demonstrated.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2001.04a
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• pp.180-189
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• 2001

In this study, recently developed unscented Kalman filter (UKF) technique is studied for identification of nonlinear structural dynamic systems as an alternative to the extended Kalman filter (EKF). The EKF, which was originally developed as a state estimator for nonlinear systems, has been frequently employed for parameter identification by introducing the state vector augmented with the unknown parameters to be identified. However, the EKF has several drawbacks such as biased estimations and erroneous estimations especially for highly nonlinear dynamic systems due to its crude linearization scheme. To overcome the weak points of the EKF, the UKF was recently developed as a state estimator. Numerical simulation studies have been carried out on nonlinear SDOF system and nonlinear MDOF system. The results from a series of numerical simulations indicate that the UKF is superior to the EKF in the system identification of nonlinear dynamic systems especially highly nonlinear systems.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.523-527
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• 2004

This paper presents a new design methods of the short-term load forecasting system (STLFS) using the data mining. The proposed predictor takes form of the convex combination of the linear time series predictors for each inputs. The problem of estimating the consequent parameters is formulated by the convex optimization problem, which is to minimize the norm distance between the real load and the output of the linear time series estimator, The problem of estimating the premise parameters is to find the parameter value minimizing the error between the real load and the overall output. Finally, to show the feasibility of the proposed method, this paper provides the short-term load forecasting example.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.30 no.8 s.251
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• pp.880-888
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• 2006

This paper presents the sliding mode control with the perturbation estimator for a nonlinear control system in the presence of perturbations including external disturbances, unpredictable parameter variations, ana unstructured dynamics. The proposed perturbation estimator is based on the Recursive Linear Smoothed Newton predictive algorithm so that it is effective to attenuate an undesired noise in high frequency band and to predict the present perturbation signal from the previous ones. Compared to conventional sliding mode control (SMC) and sliding mode control with perturbation estimation (SMCPE) introduced by Elmali and Olgac, the control algorithm proposed in this study can offer better tracking control performances and more feasible estimation characteristics. The effectiveness and superiority of the proposed control strategy are demonstrated by a series of simulations on the position tracking control of a simple two-link robot manipulator subject to velocity feedback signals including white noises.

• Proceedings of the Korean Reliability Society Conference
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• 2002.06a
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• pp.367-386
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• 2002

The Present paper is devoted to a study of the performance, in large samples, of a conditional maximum likelihood estimator(CMLE) for the parameter ${\lambda}$ in a pure birth processes(PBP). To conduct the conditional inference for the PBP, we drove the likelihood function of time-inhomogeneous Poisson processes. The limiting distributions of CMLE under the likelihoods $L_{t}$ or $\overline{L_{t}}$ are investigated. We found that the CMLE is asymptotically efficient with respect to the both $L_{t}$ or $\overline{L_{t}}$ under the efficiency criterion of Weiss & Wolfowitz(1974).

• The Korean Journal of Applied Statistics
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• v.17 no.2
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• pp.303-310
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• 2004

We suggest a method to procure information from the sensitive population which combine a direct survey method, BB and an indirect survey one, RRT, and a combined estimator that uses the stratified double sampling to estimate the sensitive parameter. We compare the efficiency of our estimator with that of Mangat and Singh model.

• Proceedings of the KIPE Conference
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• 2002.07a
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• pp.289-292
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• 2002

A sensorless control of a PM synchronous motor under the parameter variation is presented. The rotor position is estimated by using the d-axis and q-axis current errors between the real system and motor model of the estimator. The stator resistance is measured at low speeds when the motor changes its rotating direction. The gains in the position estimator are also adapted according to the motor speeds.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.12
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• pp.3055-3070
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• 2013

This paper proposes an improved carrier frequency offset (CFO) and sampling frequency offset (SFO) estimation scheme for orthogonal frequency division multiplexing (OFDM) based broadcasting system with cyclic delay diversity (CDD) antenna. By exploiting a periodic nature of channel transfer function, cyclic delay and pilot pattern with a maximum channel power are carefully chosen, which helps to enable a robust estimation of CFO and SFO against the frequency selectivity of the channel. As a performance measure, a closed-form expression for the achievable mean square error of the proposed scheme is derived and is verified through simulations using the parameters of the digital radio mondiale standard. The comparison results show that the proposed frequency estimator is shown to benefit from properly selected delay parameter and pilot pattern, with a performance better than the existing estimator.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2001.10a
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• pp.117-126
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• 2001

In this study, the recently developed unscented Kalman filter (UKF) technique is studied for identification of nonlinear structural dynamic systems as an alternative to the extended Kalman filter (EKF). The EKF, which was originally developed as a state estimator for nonlinear systems, has been frequently employed for parameter identification by introducing the state vector augmented with the unknown parameters to be identified. However, the EKF has several drawbacks such as biased estimations and erroneous estimations especially for highly nonlinear dynamic systems due to its crude linearization scheme. To overcome the weak points of the EKF, the UKF was recently developed as a state estimator. Numerical simulation studies have been carried out on nonlinear SDOF system and nonlinear MDOF system. The results from a series of numerical simulations indicate that the UKF is superior to the EKF in the system identification of nonlinear dynamic systems especially highly nonlinear systems.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.2
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• pp.125-130
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• 2001

This paper presents an indirect field-oriented (IFO) induction motor position servo drives which uses the model following adaptive controller with the artificial neural network(ANN)-based rotor resistance estimator. The model reference adaptive system(MRAS)-based 2-layer ANN estimates the rotor resistance on-line and a linear model-following position controller is designed by using the estimated the rotor resistance value. At the end, a fuzzy logic system(FLS) is added to make the position controller robust to the external disturbances and the parameter variations. The simulation results show the effectiveness of the proposed method.