• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.6
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• pp.228-236
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• 2014

Towed array SONAR is deformed because it operates in fluid such as an ocean. It especially undergoes significant change in shape as a towing vessel takes a turn. In this case, beam pattern synthesis of the line array is limited, resulting in degradation in quality such as signal-to-noise ratio. This paper presents a modified two-step least squares algorithm based on the two-step least squares method. The shape of the sea-operated line array formation with the towing vessel changing course(angle) was modeled and the algorithm was subsequently applied. While changing course and location of the main lobe in beam pattern was altered, signal-to-noise ratio of steering beam pattern synthesis was analyzed by algorithm (proposed and others). As a result, the proposed algorithm presented improvement in performance by 2dB compared to other algorithms while forming relatively constant beam pattern.

• Journal of the Korean Data and Information Science Society
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• v.23 no.2
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• pp.393-401
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• 2012

This paper deals with the estimations of the least squares support vector regression when the responses are subject to randomly right censoring. The estimation is performed via two steps - the ordinary least squares support vector regression and the least squares support vector regression with censored data. We use the empirical fact that the estimated regression functions subject to randomly right censoring are close to the true regression functions than the observed failure times subject to randomly right censoring. The hyper-parameters of model which affect the performance of the proposed procedure are selected by a generalized cross validation function. Experimental results are then presented which indicate the performance of the proposed procedure.

• Smart Structures and Systems
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• v.15 no.1
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• pp.57-80
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• 2015

Extended Kalman Filter (EKF) has been widely used for structural identification and damage detection. However, conventional EKF approaches require that external excitations are measured. Also, in the conventional EKF, unknown structural parameters are included as an augmented vector in forming the extended state vector. Hence the sizes of extended state vector and state equation are quite large, which suffers from not only large computational effort but also convergence problem for the identification of a large number of unknown parameters. Moreover, such approaches are not suitable for intelligent structural damage detection due to the limited computational power and storage capacities of smart sensors. In this paper, a two-stage and two-step algorithm is proposed for the identification of structural damage as well as unknown external excitations. In stage-one, structural state vector and unknown structural parameters are recursively estimated in a two-step Kalman estimator approach. Then, the unknown external excitations are estimated sequentially by least-squares estimation in stage-two. Therefore, the number of unknown variables to be estimated in each step is reduced and the identification of structural system and unknown excitation are conducted sequentially, which simplify the identification problem and reduces computational efforts significantly. Both numerical simulation examples and lab experimental tests are used to validate the proposed algorithm for the identification of structural damage as well as unknown excitations for structural health monitoring.

• The Korean Journal of Applied Statistics
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• v.34 no.2
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• pp.239-254
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• 2021

This paper studies time series analysis with estimation and forecasting for Korean COVID-19 confirmed cases, based on the approach of a heterogeneous autoregressive (HAR) model with two-piece t (TP-T) distributed errors. We consider HAR-TP-T time series models and suggest a step-by-step method to estimate HAR coefficients as well as TP-T distribution parameters. In our proposed step-by-step estimation, the ordinary least squares method is utilized to estimate the HAR coefficients while the maximum likelihood estimation (MLE) method is adopted to estimate the TP-T error parameters. A simulation study on the step-by-step method is conducted and it shows a good performance. For the empirical analysis on the Korean COVID-19 confirmed cases, estimates in the HAR-TP-T models of order p = 2, 3, 4 are computed along with a couple of selected lags, which include the optimal lags chosen by minimizing the mean squares errors of the models. The estimation results by our proposed method and the solely MLE are compared with some criteria rules. Our proposed step-by-step method outperforms the MLE in two aspects: mean squares error of the HAR model and mean squares difference between the TP-T residuals and their densities. Moreover, forecasting for the Korean COVID-19 confirmed cases is discussed with the optimally selected HAR-TP-T model. Mean absolute percentage error of one-step ahead out-of-sample forecasts is evaluated as 0.0953% in the proposed model. We conclude that our proposed HAR-TP-T time series model with optimally selected lags and its step-by-step estimation provide an accurate forecasting performance for the Korean COVID-19 confirmed cases.

• Proceedings of the Korean Statistical Society Conference
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• 2003.05a
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• pp.269-273
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• 2003

We consider an efficient parametric estimation method of spatial dependence in weak stationary processes. Spatial dependence is modeled through variogram and correlogram. Most of parametric estimation methods of correlogram use two step method; nonparametric estimation and parametric integration. We bind these two steps into one step by using GEE method instead of least squares type optimization. Our one step method is more efficient statistically and gives a clear interpretation of related concepts used in traditional two step methods.

• International Journal of Human Ecology
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• v.4 no.2
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• pp.77-87
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• 2003

This paper tried to construct statistical and econometric models on the basis of economic theory in order to discuss the issue of statistical efficiency and unbiasedness including the sample selection bias correcting problem. Comparative analytical tool were one stage Tobit of Maximum Likelihood estimation and Heckman's two-step Tobit of Ordinary Least Squares. The results showed that the adequacy of model for the analysis on demand and choice, we believe that there is no big difference in explanatory variables between the first selection model and the second linear probability model. Since the Lambda, the self- selectivity correction factor, in the Type II Tobit is not statistically significant, there is no self-selectivity in the Type II Tobit model, indicating that Type I Tobit model would give us better explanation in the demand for and choice which is less complicated statistical method rather than type II model.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10b
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• pp.1854-1857
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• 1991

A numerically efficient modification of a variable step size LMS (Least Mean Squares) algorithm is proposed. This proposed algorithm is very simple in calculation and has a variable step size adjusted by the filter output error. Its additional computational burden with respect to the conventional LMS algorithm is only two multiplications, two substraction, an addition and some bit operations. In a simulation example, it is shown that the proposed algorithm has not only the faster convergence rate but also less misadjustments in the environment of highly nonstationary and correlated data.

• Journal of Convergence for Information Technology
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• v.10 no.9
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• pp.15-21
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• 2020

This paper presents the outlier detection algorithm in the estimation method of a source location and velocity based on two-step weighted least-squares method using time difference of arrival(TDOA) and frequency difference of arrival(FDOA) data. Since the accuracy of the estimated location and velocity of a moving source can be reduced by the outliers of TDOA and FDOA data, it is important to detect and remove the outliers. In this paper, the method to find the minimum inlier data and the method to determine whether TDOA and FDOA data are included in inliers or outliers are presented. The results of numerical simulations show that the accuracy of the estimated location and velocity is improved by removing the outliers of TDOA and FDOA data.

• Geophysics and Geophysical Exploration
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• v.10 no.2
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• pp.147-153
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• 2007

The conjugate gradient (CG) method is one of the most efficient algorithms for solving a linear system of equations. In addition to being used as a linear equation solver, it can be applied to a least-squares problem. When the CG method is applied to large-scale three-dimensional inversion of magnetotelluric data, two approaches have been pursued; one is the linear CG inversion in which each step of the Gauss-Newton iteration is incompletely solved using a truncated CG technique, and the other is referred to as the nonlinear CG inversion in which CG is directly applied to the minimization of objective functional for a nonlinear inverse problem. In each procedure we only need to compute the effect of the sensitivity matrix or its transpose multiplying an arbitrary vector, significantly reducing the computational requirements needed to do large-scale inversion.

• The Korean Journal of Applied Statistics
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• v.4 no.1
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• pp.13-24
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• 1991

This paper presents a Monte Carlo evaluation of estimators for nonlinear consored simultaneous equations models. We examine the performance of the maximum likelihood estimator (MLE), the two-step quasi maximum likelihood estimator (2QMLE) proposed by Lee and Hurd (1989), and another quasi MLe using least squares at the first step (LSAE) under varying degrees of freedom and underlying distributions, Although QMLE's are not necessarily consistent, the Monte Carlo results show that the 2QMLE may be used as an alternative to MLE when MLE is not applicable in practice.