• Communications for Statistical Applications and Methods
• /
• v.12 no.2
• /
• pp.285-294
• /
• 2005

This paper deals with the comparison of parameter estimation methods in a 3-parameter Kappa distribution which is sometimes used in flood frequency analysis. Method of moment estimation(MME), L-moment estimation(L-ME), and maximum likelihood estimation(MLE) are applied to estimate three parameters. The performance of these methods are compared by Monte-carlo simulations. Especially for computing MME and L-ME, three dimensional nonlinear equations are simplified to one dimensional equation which is calculated by the Newton-Raphson iteration under constraint. Based on the criterion of the mean squared error, L-ME (or MME) is recommended to use for small sample size( n$\le$100) while MLE is good for large sample size.

• Journal of Electrical Engineering and Technology
• /
• v.4 no.3
• /
• pp.360-364
• /
• 2009

This paper suggests the techniques in determining the values of the steady-state equivalent circuit parameters of a three-phase induction machine using genetic algorithm. The parameter estimation procedure is based on the steady-state phase current versus slip and input power versus slip characteristics. The propose estimation algorithm is of non-linear kind based on selection in genetic algorithm. The machine parameters are obtained as the solution of a minimization of objective function by genetic algorithm. Simulation shows good performance of the propose procedures.

• Communications for Statistical Applications and Methods
• /
• v.14 no.2
• /
• pp.345-353
• /
• 2007

A Bayesian estimation of the Nakagami-m fading parameter is developed. Bayesian estimation is performed by Gibbs sampling, including adaptive rejection sampling. A Monte Carlo study shows that the Bayesian estimators proposed outperform any other estimators reported elsewhere in the sense of bias, variance, and root mean squared error.

• The Transactions of the Korean Institute of Electrical Engineers
• /
• v.39 no.4
• /
• pp.380-385
• /
• 1990

This Paper presents a general algorithm for the parameter estimation of an antoregressive moving average process observed in additive white noise. The algorithm is based on the Gauss-Newton recursive prediction error method. For the parameter estimation, the output measurement is modelled as an innovation process using the spectral factorization, so that noise free RPE ARMA estimation can be used. Using apriori known properties leads to algorithm with smaller computation and better accuracy be the parsimony principle. Computer simulation examples show the effectiveness of the proposed algorithm.

• The Journal of the Acoustical Society of Korea
• /
• v.15 no.1E
• /
• pp.65-69
• /
• 1996

This paper presents an algorithm for multipath channel parameter estimation by an improved Prony method. This algorithm applies a modified regularized spectral estimation to the conventional SVD Prony method. This method requires no a priori information on the number of multipath. The performance of the proposed algorithm is almost the same as that of the SVD based multipath channel parameter estimation algorithm.

• Proceedings of the KIEE Conference
• /
• 1999.11b
• /
• pp.279-281
• /
• 1999

This paper addresses the issues of the parameter error detection and identification in power system. The parameter error identification is carried out as part of the state estimation procedure. The weighted least absolute value(WLAV) estimation method is used for this procedure. The standard formulation of the state estimation problem is modified to include the effects of the parameter errors as well. A two step procedure for the detection and identification of faulted parameters is proposed. Supporting examples are given using IEEE 14 bus system.

• Journal of the Korean Statistical Society
• /
• v.31 no.1
• /
• pp.93-107
• /
• 2002

Random effects generalised linear models are useful for analysing clustered count data in which responses are usually correlated. We propose a Bayesian approach to parameter estimation and variable selection in random effects generalised linear models for count data. A simple Gibbs sampling algorithm for parameter estimation is presented and a simple and efficient variable selection is done by using the Gibbs outputs. An illustrative example is provided.

• Journal of the Korean Society of Hazard Mitigation
• /
• v.10 no.6
• /
• pp.119-130
• /
• 2010

The universal model for the parameter estimation of the Storage Function Model(SFM) was developed through the applications of the distributed model for various hypothetical watersheds and runoff conditions. The existing parameter estimation equations are based on observations and these equations which are derived from the restricted conditions are not sensitive to the variation of physical characteristics of a watershed. This study developed the universal model for the parameter estimation through the runoff simulations of 35,000 times. As the simulation results, we have known that the lag time is related to the longest stream channel characteristics and the storage coefficient is related to the watershed characteristics.

• Transactions of the Korean Society of Mechanical Engineers
• /
• v.8 no.1
• /
• pp.65-70
• /
• 1984

A recursive parameter estimation method is applied to spindle deflection model during boring process. The spindle infeed rate is then determined to preserve the diametral tolerance of bore. This estimation method is further extended to adaptive control by application of the variance perturbation method. The results of computer simulation attest that the proposed method renders the optimal cutting conditions, maintaining the diametral accuracy of bore, regardless of parameter fluctuations. The proposed method necessitating only post-process measurements features that initialization of parameter guess values in simple, a priori knowledge on parameter variations is not needed and the accurate estimation of optimal spindle infeed rate is obtained, even if the parameter estimation may be poor.

• Journal of Electrical Engineering and Technology
• /
• v.3 no.4
• /
• pp.451-459
• /
• 2008

Accurate load modeling is essential for power system static and dynamic analysis. By the nature of the problem of parameter estimation for power system load modeling using actual measurements, multiple local optimal solutions may exist and local methods can be trapped in a local optimal solution giving possibly poor performance. In this paper, Trust-Tech, a novel methodology for global optimization, is applied to tackle the multiple local optimal solutions issue in measurement-based power system load modeling. Multiple sets of parameter values of a composite load model are obtained using Trust-Tech in a deterministic manner. Numerical studies indicate that Trust-Tech along with conventional local methods can be successfully applied to power system load model parameter estimation in measurement-based approaches.