Linearly Supporting Feature Extraction For Automated Estimation Of Stellar Atmospheric Parameters

We describe a scheme to extract linearly supporting (LSU) features from stellar spectra to automatically estimate the atmospheric parameters $T_{eff}$, log$~g$, and [Fe/H]. "Linearly supporting" means that the atmospheric parameters can be accurately estimated from the extracted features through a linear model. The successive steps of the process are as follow: first, decompose the spectrum using a wavelet packet (WP) and represent it by the derived decomposition coefficients; second, detect representative spectral features from the decomposition coefficients using the proposed method Least Absolute Shrinkage and Selection Operator (LARS)$_{bs}$; third, estimate the atmospheric parameters $T_{eff}$, log$~g$, and [Fe/H] from the detected features using a linear regression method. One prominent characteristic of this scheme is its ability to evaluate quantitatively the contribution of each detected feature to the atmospheric parameter estimate and also to trace back the physical significance of that feature. This work also shows that the usefulness of a component depends on both wavelength and frequency. The proposed scheme has been evaluated on both real spectra from the Sloan Digital Sky Survey (SDSS)/SEGUE and synthetic spectra calculated from Kurucz's NEWODF models. On real spectra, we extracted 23 features to estimate $T_{eff}$, 62 features for log$~g$, and 68 features for [Fe/H]. Test consistencies between our estimates and those provided by the Spectroscopic Sarameter Pipeline of SDSS show that the mean absolute errors (MAEs) are 0.0062 dex for log$~T_{eff}$ (83 K for $T_{eff}$), 0.2345 dex for log$~g$, and 0.1564 dex for [Fe/H]. For the synthetic spectra, the MAE test accuracies are 0.0022 dex for log$~T_{eff}$ (32 K for $T_{eff}$), 0.0337 dex for log$~g$, and 0.0268 dex for [Fe/H].Comment: 21 pages, 7 figures, 8 tables, The Astrophysical Journal Supplement Series (accepted for publication

Fractional-Order Time Delay Compensation in Deadbeat Control for Power Converters

Deadbeat control scheme is widely implemented in the control of power electronics and electrical drives, which is of simplification, rapidity and flexibility. However, owing to its sensitive to model uncertainties and unmodeled dynamics, the practical control performance is severely degraded and sometimes even unstable. Uncertain time delay is a typical case of model uncertainties, which severely deteriorates the control accuracy and dramatically reduce the system stability margin of deadbeat control. In this paper, the time delay effects on the control performance and system stability are investigated. A fractional-order Smith Predictor based solution is proposed to compensate arbitrary time delay with high accuracy, simple structure, and good robustness. The composite control scheme offers accurate time delay compensations in digital implementation and considerably enhances the robustness of the control system, which will effectively promote widespread applications of the deadbeat scheme. An application example of three-phase inverter system is explored to comprehensively illustrate the feasibility and effectiveness of the proposed scheme