A Joint Distribution System State Estimation Framework via Deep Actor-Critic Learning Method
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Calculations modeling the effects of stellar evolution on the observed colors of elliptical galaxies suggest that the shape of their spectra should remain almost constant in the red and near-infrared for red shifts greater than two. In order to test this idea, JHK photometry of optically faint radio source identifications believed to be faint elliptical galaxies were carried out. Photometric redshift estimates have been made using three different models: a nonevolving elliptical galaxy spectrum, an evolving spectrum based on one of Bruzual's calculations, and an empirical model derived from photometry of distant elliptical galaxies. Redshifts greater than 0.5 are systematically under-estimated by the first two models and for galaxies whose colors are not consistent with the models, deviations occur not only in the optical bands but also in the infrared. The difficulties are not resolved by the addition of a quasar-like nucleus to the galaxies.
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Estimation in truncated Poisson distributions with concomitant exposure intervals and truncation points
Table yields best linear invariant /BLI/ estimates for log of reliable life under censored life tests, permitting reliability estimations in failure analysis of items with multiple flaws. These BLI estimates have uniformly smaller expected loss than Gauss-Markov best linear unbiased estimates.
We present an abstract approximation framework for estimation of parameters in nonlinear nonautonomous distributed systems. Specific examples involving linear spline approximations for retarded delay equations and cubic spline approximations for parabolic partial differential equations are discussed and shown to be included as special cases of our general framework.
A method to estimate raindrop size distribution (DSD) parameters from a combined Zm profile and path-integrated attenuation is shown, and a test result of the method using the data from an aircraft experiment is presented. The 'semi' dual-parameter (SDP) measurement is employed to estimate DSD parameters using the data obtained from an aircraft experiment conducted by Communications Research Laboratory, Tokyo, in conjunction with NASA. The validity of estimated DSD parameters is examined using measured Ka-band radar reflectivities. The estimated path-averaged N(0) is consistent with the Ka/X Ze ratio, and the use of estimated DSD shows excellent agreement between the rain rates estimated from the X-band and K-band Zes. The feasibility of estimating DSD parameters from space is confirmed.
Smooth empirical Bayes estimation with application to Weibull distribution
The increasing penetration of behind-the-meter PVs causes challenges to maintain voltage security due to the lack of distribution system visibility. This paper proposes a nonlinear autoregressive Gaussian process (NARGP) approach to fuse limited number of SCADA/AMI data together with historical pseudo measurements for distribution node voltage probabilistic estimation. The high-fidelity SCADA data are fused with the low-fidelity AMI and pseudo measurements by the autoregressive algorithm embedded in the Gaussian process. This allows us to use multi-fidelity data to achieve entire distribution system voltage visibility. Numerical results carried out on the IEEE 123node system demonstrate that the NARGP method is able to obtain high accuracy in estimating bus voltage and quantifying estimation uncertainties as compared to other approaches.