• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1998.06a
• /
• pp.680-684
• /
• 1998

This paper presents an efficient method which estimates the systems with unmodeled dynamics using D-L networks. This method is applied for estimating the system with unmodeled dynamics from only input-output information , so it can exclude additional procedure for system description and reduce the computational burden required for real-time estimation. Higher convergence speed is achieved in this manner in comparison with widely-used conventional methods.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• v.1
• /
• pp.485-489
• /
• 2006

In last decades, several linearization methods for the AOA measurements have been proposed, for example, Gauss-Newton method and closed-form solution. Gauss-Newton method can achieve high accuracy, but the convergence of the iterative process is not always ensured if the initial guess is not accurate enough. Closed-form solution provides a non-iterative solution and it is less computational. It does not suffer from convergence problem, but estimation error is somewhat larger. This paper proposes a self-tuning weighted least square AOA algorithm that is a modified version of the conventional closed-form solution. In order to estimate the error covariance matrix as a weight, two-step estimation technique is used. Simulation results show that the proposed method has smaller positioning error compared to the existing methods.

• Journal of Positioning, Navigation, and Timing
• /
• v.12 no.2
• /
• pp.157-166
• /
• 2023

Several experiments were carried out to analyze the impact of the modernized Global Positioning System (GPS) L2C signal on pseudorange-based point positioning. Three dual-frequency positioning algorithms, ionosphere-free linear combination, ionospheric error estimation, and simple integration, were used, and the results were compared with those of Standard Point Positioning (SPP). An analysis was conducted to determine the characteristics of each dual-frequency positioning method, the impact of the magnitude of ionospheric error, and receiver grade. Ionosphere-free and ionospheric error estimation methods can provide improved positioning accuracy relative to SPP because they are able to significantly reduce the ionospheric error. However, this result was possible only when the ionospheric error reduction effect was greater than the disadvantage of these dual-frequency positioning algorithms such as the increment of multipath and noise, impact of uncertainty of unknown parameter estimation. The RMSE of the simple integration algorithm was larger than that of SPP, because of the remaining ionospheric error. Even though the receiver grade was different, similar results were observed.

• The Pure and Applied Mathematics
• /
• v.9 no.1
• /
• pp.81-90
• /
• 2002

The least informative (favorable) distributions, minimizing Fisher information for a multivariate location parameter, are derived in the parametric class of the exponential-power spherically symmetric distributions under the following characterizing restrictions; (i) a bounded variance, (ii) a bounded value of a density at the center of symmetry, and (iii) the intersection of these restrictions. In the first two cases, (i) and (ii) respectively, the least informative distributions are the Gaussian and Laplace, respectively. In the latter case (iii) the optimal solution has three branches, with relatively small variances it is the Gaussian, them with intermediate variances. The corresponding robust minimax M-estimators of location are given by the $L_2$-norm, the $L_1$-norm and the $L_{p}$ -norm methods. The properties of the proposed estimators and their adaptive versions ar studied in asymptotics and on finite samples by Monte Carlo.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
• /
• 2009.05a
• /
• pp.368-371
• /
• 2009

Earth parameters me essential to design and analysis of earth. In this study, a algorithm to estimate earth parameter using artificial neural network(ANN) was proposed. Structures of the soil are grouped by using KSOM algorithm before estimation. Earth parameter is obtained by using BP algorithm. The effectiveness of the proposed algorithm was verified in the case study.

• Communications for Statistical Applications and Methods
• /
• v.11 no.3
• /
• pp.643-655
• /
• 2004

L$_1$-estimator in the linear regression model is widely recognized to have superior robustness in the presence of vertical outliers. While the L$_1$-estimation procedures and algorithms have been developed quite well, less progress has been made with the hypothesis test in the multiple L$_1$-regression. This article suggests computer-intensive resampling approaches, jackknife and bootstrap methods, to estimating the variance of L$_1$-estimator and the scale parameter that are required to compute the test statistics. Monte Carlo simulation studies are performed to measure the power of tests in small samples. The simulation results indicate that bootstrap estimation method is the most powerful one when it is employed to the likelihood ratio test.

• The Korean Journal of Applied Statistics
• /
• v.29 no.3
• /
• pp.471-485
• /
• 2016

Least squares (LS) regression is a classic method for regression that is optimal under assumptions of regression and usual observations. However, the presence of unusual data in the LS method leads to seriously distorted estimates. Therefore, various robust estimation methods are proposed to circumvent the limitations of traditional LS regression. Among these, there are M-estimators based on maximum likelihood estimation (MLE), L-estimators based on linear combinations of order statistics and R-estimators based on a linear combinations of the ordered residuals. In this paper, robust regression estimators with high breakdown point and/or with high efficiency are compared under several simulated situations. The paper analyses and compares distributions of estimates as well as relative efficiencies calculated from mean squared errors (MSE) in the simulation study. We conclude that MM-estimators or GR-estimators are a good choice for the real data application.

• The Transactions of the Korean Institute of Power Electronics
• /
• v.24 no.6
• /
• pp.445-451
• /
• 2019

An input power estimation method of a three-phase inverter for the high-efficiency operation of AC motors is proposed. Measuring devices, such as DC link voltage and input current sensors, are required to obtain the input power of the inverter. In the proposed method, the input power of the inverter can be estimated without the input current sensor by using the phase current information of the AC motor and the switching pattern of the inverter. The proposed method is more robust to parameter error than conventional method. The validity of the input power estimation method is verified through experiments conducted on a 1 kW permanent-magnet synchronous motor drive system.

• KCID journal
• /
• v.14 no.1
• /
• pp.100-109
• /
• 2007

• Proceedings of the KIEE Conference
• /
• 1994.11a
• /
• pp.36-38
• /
• 1994

A novel UHS relaying algorithm using reflection coefficients estimation is proposed. Based on a travelling ware approach the algorithm can determine the fault location in a travelling time of the protected lone. The discrimation of the reflected wave from others is possible observing the difference of two coefficients. The algorithm is tested using results determined by EMTP.