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Grid synchronization plays an important role in the grid integration of renewable energy sources. To achieve grid synchronization, accurate information of the grid voltage signal parameters are needed. Motivated by this important ...
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Grid synchronization plays an important role in the grid integration of renewable energy sources. To achieve grid synchronization, accurate information of the grid voltage signal parameters are needed. Motivated by this important practical application, this paper proposes a state observer-based approach for the parameter estimation of unbalanced three-phase grid voltage signal. The proposed technique can extract the frequency of the distorted grid voltage signal and is able to quantify the grid unbalances. First, a dynamical model of the grid voltage signal is developed considering the disturbances. In the model, frequency of the grid is considered as a constant and/or slowly-varying but unknown quantity. Based on the developed dynamical model, a state observer is proposed. Then using Lyapunov function-based approach, a frequency adaptation law is proposed. The chosen frequency adaptation law guarantees the global convergence of the estimation error dynamics and as a consequence, ensures the global asymptotic convergence of the estimated parameters in the fundamental frequency case. Gain tuning of the proposed state observer is very simple and can be done using Matlab commands. Some guidelines are also provided in this regard. Matlab/Simulink based numerical simulation results and dSPACE 1104 board-based experimental results are provided. Test results demonstrate the superiority and effectiveness of the proposed approach over another state-of-the art technique.
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The extended exponential distribution due to Nadarajah and Haghighi (Stat J Theor Appl Stat 45(6):543-558,2011) is an alternative and always provides better fits than the gamma, Weibull and the generalized exponential distribution...
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The extended exponential distribution due to Nadarajah and Haghighi (Stat J Theor Appl Stat 45(6):543-558,2011) is an alternative and always provides better fits than the gamma, Weibull and the generalized exponential distributions whenever the data contains zero values. This article addresses different methods of estimation of the unknown parameters from both frequentist and Bayesian view points of Nadarajah and Haghighi (in short NH ) distribution. We briefly describe different frequentist approaches, namely, maximum likelihood estimators, moment estimators, percentile estimators, least square and weighted least square estimators and compare them using extensive numerical simulations. Next we consider Bayes estimation under different types of loss functions (symmetric and asymmetric loss functions) using gamma priors for both shape and scale parameters. Besides, the asymptotic confidence intervals, two parametric bootstrap confidence intervals using frequentist approaches are provided to compare with Bayes credible intervals. Furthermore, the Bayes estimators and their respective posterior risks are computed and compared using Markov chain Monte Carlo algorithm. Finally, two real data sets have been analyzed for illustrative purposes.
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For discrete-time linear systems, we propose a suboptimal approach to constrained estimation so that the associated computation burden is reduced. This is achieved by enforcing a move blocking (MB) structure in the estimated proce...
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For discrete-time linear systems, we propose a suboptimal approach to constrained estimation so that the associated computation burden is reduced. This is achieved by enforcing a move blocking (MB) structure in the estimated process noise sequence (PNS). We show that full information estimation (FIE) and receding horizon estimation (RHE) with MB are both stable in the sense of an observer. The techniques in proving stability are inspired by those that have been proposed for standard RHE. To be specific, stability results are mainly achieved by (i) carefully embellishing the general assumptions for standard RHE to accommodate the MB requirement; (ii) exploiting the principle of optimality, as well as convexity of the quadratic programs (QPs) associated with FIE and RHE; (iii) relying on the fact that the Kalman filter is the best linear estimator in the least-squares sense. A crucial requirement in achieving stability for MB RHE is that the segment structure (SS) of the PNS of MB FIE for the optimization steps within the receding horizon (i.e., steps between T - N and T - 1) has to be enforced in the MB RHE optimization. As a result, the MB RHE strategy becomes a dynamic estimator with a periodically varying computational complexity. The theoretical results have been illustrated with examples. (C) 2018 Elsevier Ltd. All rights reserved.
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We consider estimation of loss for generalized Bayes or pseudo-Bayes estimators of a multivariate normal mean vector, θ. In 3 and higher dimensions, the MLEX is UMVUE and minimax but is inadmissible. It is dominated by the James-...
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We consider estimation of loss for generalized Bayes or pseudo-Bayes estimators of a multivariate normal mean vector, θ. In 3 and higher dimensions, the MLEX is UMVUE and minimax but is inadmissible. It is dominated by the James-Stein estimator and by many others. Johnstone (1988, On inadmissibility of some unbiased estimates of loss,Statistical Decision Theory and Related Topics, IV (eds. S. S. Gupta and J. O. Berger), Vol. 1, 361–379, Springer, New York) considered the estimation of loss for the usual estimatorX and the James-Stein estimator. He found improvements over the Stein unbiased estimator of risk. In this paper, for a generalized Bayes point estimator of θ, we compare generalized Bayes estimators to unbiased estimators of loss. We find, somewhat surprisingly, that the unbiased estimator often dominates the corresponding generalized Bayes estimator of loss for priors which give minimax estimators in the original point estimation problem. In particular, we give a class of priors for which the generalized Bayes estimator of θ is admissible and minimax but for which the unbiased estimator of loss dominates the generalized Bayes estimator of loss. We also give a general inadmissibility result for a generalized Bayes estimator of loss.
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We use simple examples to show how the bias and standard error of an estimator depend in part on the type of estimator chosen from among parametric, nonparametric, and semiparametric candidates. We estimated the cumulative distrib...
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We use simple examples to show how the bias and standard error of an estimator depend in part on the type of estimator chosen from among parametric, nonparametric, and semiparametric candidates. We estimated the cumulative distribution function in the presence of missing data with and without an auxiliary variable. Simulation results mirrored theoretical expectations about the bias and precision of candidate estimators. Specifically, parametric maximum likelihood estimators performed best but must be "omnisciently" correctly specified. An augmented inverse probability-weighted (IPW) semiparametric estimator performed best among candidate estimators that were not omnisciently correct. In one setting, the augmented IPW estimator reduced the standard error by nearly 30%, compared with a standard Horvitz-Thompson IPW estimator; such a standard error reduction is equivalent to doubling the sample size. These results highlight the gains and losses that can be incurred when model assumptions are made in any analysis.
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The problem of estimating ordered parameters arises widely in biological, agricultural, economic, and reliability experiments. Consider a bivariate normal population with unknown mean (Θ_1, Θ_2), known variances, and known corre...
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The problem of estimating ordered parameters arises widely in biological, agricultural, economic, and reliability experiments. Consider a bivariate normal population with unknown mean (Θ_1, Θ_2), known variances, and known correlation coefficient p, where Θ_1 ≤ Θ_2. The problem of estimation of (Θ_1, Θ_2) is studied when the loss function used is sum of squared errors. We have considered two cases: equal variances and unequal variances. In both cases, a class of minimax estimators is proposed. These estimators improve upon the usual estimators. A class of admissible estimators is obtained within this class. The minimaxity and admissibility of a generalized Bayes estimator is established. Finally, the risk performance of all the proposed estimators has been compared numerically.
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Calibration estimation, where the sampling weights are adjusted to make certain estimators match known population totals, is commonly used in survey sampling. The generalized regression estimator is an example of a calibration est...
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Calibration estimation, where the sampling weights are adjusted to make certain estimators match known population totals, is commonly used in survey sampling. The generalized regression estimator is an example of a calibration estimator. Given the functional form of the calibration adjustment term, we establish the asymptotic equivalence between the functional-form calibration estimator and an instrumental variable calibration estimator where the instrumental variable is directly determined from the functional form in the calibration equation. Variance estimation based on linearization is discussed and applied to some recently proposed calibration estimators. The results are extended to the estimator that is a solution to the calibrated estimating equation. Results from a limited simulation study are presented.
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Software enhancements and the maintenance phase is generally the crucial phase of a software application lifecycle. The enhancements and maintenance consume about 20% of the overall software lifecycle effort. Enhancement and maint...
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Software enhancements and the maintenance phase is generally the crucial phase of a software application lifecycle. The enhancements and maintenance consume about 20% of the overall software lifecycle effort. Enhancement and maintenance phase of modern digital projects involves many activities such as incident management, application enhancements, generic maintenance, quality improvements such as automation, preventive maintenance, continuous improvement, and such. State-of the-art estimation models and frameworks fall short of factoring all the dynamics involved in the enhancements and maintenance phase. The article proposes a digital project maintenance estimation framework to estimate various activities of a digital maintenance project. The proposed estimation framework provides comprehensive coverage of maintenance activities including incident management, application enhancements, generic maintenance, and quality improvements. The proposed estimation framework was used to predict effort estimate of 5 digital maintenance projects with MMRE of 0.255 and predicted (0.3) of 80%.
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Since the seminal work of Stein in the 1950s, there has been continuing research devoted to improving the total mean-squared error (MSE) of the least-squares (LS) estimator in the linear regression model. However, a drawback of th...
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Since the seminal work of Stein in the 1950s, there has been continuing research devoted to improving the total mean-squared error (MSE) of the least-squares (LS) estimator in the linear regression model. However, a drawback of these methods is that although they improve the total MSE, they do so at the expense of increasing the MSE of some of the individual signal components. Here we consider a framework for developing linear estimators that outperform the LS strategy over bounded norm signals, under all weighted MSE measures. This guarantees, for example, that both the total MSE and the MSE of each of the elements will be smaller than that resulting from the LS approach. We begin by deriving an easily verifiable condition on a linear method that ensures LS domination for every weighted MSE. We then suggest a minimax estimator that minimizes the worst-case MSE over all weighting matrices and bounded norm signals subject to the universal weighted MSE domination constraint.
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Using internally consistent albedo, aerosol, cloud, and surface data from the Multiangle Imaging Spectroradiometer (MISR) instrument onboard the Terra satellite, top-of-atmosphere (TOA) spectral albedo change (dα) in the presence...
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Using internally consistent albedo, aerosol, cloud, and surface data from the Multiangle Imaging Spectroradiometer (MISR) instrument onboard the Terra satellite, top-of-atmosphere (TOA) spectral albedo change (dα) in the presence of aerosols over land is estimated and its dependence on aerosol and surface properties is analyzed. Linear regressions between spectral TOA albedo and aerosol optical depth (AOD) for different surface types are examined to derive the aerosol-free TOA albedo. MISR surface BiHemispherical Reflectance (BHR) values are used to differentiate surface types. We find relatively high correlations between spectral TOA albedo and AOD for BHR-stratified data in 2° x 2° grid cells. The global mean values of cloud-free da over land for June-September 2007 are estimated to be 0.018 + 0.003 (blue), 0.010 ± 0.003 (green), 0.007 ± 0.003 (red), and 0.008 ± 0.006 (near-infrared). Individual regions show large variations from these values. Global patterns of dα are determined mainly by AOD and aerosol radiative efficiency. Large positive values of da are observed over regions with high aerosol loading and large single-scattering albedo, where the aerosol scattering effect is dominant. The presence of light-absorbing aerosols reduces aerosol radiative efficiency and dα. Surface reflectance influences both aerosol scattering and absorbing effects. Generally, the aerosol radiative efficiency decreases with increasing BHR. We also examined dα-AOD correlations over different vegetation types. We find the smallest dα values are over needleleaf forests and shrublands, whereas the largest values are over cropland and barren regions. The aerosol radiative efficiencies are lowest over needleleaf forests and barren regions and highest over grasslands and croplands.
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