34,748 research outputs found
Attitude Estimation and Control Using Linear-Like Complementary Filters: Theory and Experiment
This paper proposes new algorithms for attitude estimation and control based
on fused inertial vector measurements using linear complementary filters
principle. First, n-order direct and passive complementary filters combined
with TRIAD algorithm are proposed to give attitude estimation solutions. These
solutions which are efficient with respect to noise include the gyro bias
estimation. Thereafter, the same principle of data fusion is used to address
the problem of attitude tracking based on inertial vector measurements. Thus,
instead of using noisy raw measurements in the control law a new solution of
control that includes a linear-like complementary filter to deal with the noise
is proposed. The stability analysis of the tracking error dynamics based on
LaSalle's invariance theorem proved that almost all trajectories converge
asymptotically to the desired equilibrium. Experimental results, obtained with
DIY Quad equipped with the APM2.6 auto-pilot, show the effectiveness and the
performance of the proposed solutions.Comment: Submitted for Journal publication on March 09, 2015. Partial results
related to this work have been presented in IEEE-ROBIO-201
Freeze-drying modeling and monitoring using a new neuro-evolutive technique
This paper is focused on the design of a black-box model for the process of freeze-drying of pharmaceuticals. A new methodology based on a self-adaptive differential evolution scheme is combined with a back-propagation algorithm, as local search method, for the simultaneous structural and parametric optimization of the model represented by a neural network. Using the model of the freeze-drying process, both the temperature and the residual ice content in the product vs. time can be determine off-line, given the values of the operating conditions (the temperature of the heating shelf and the pressure in the drying chamber). This makes possible to understand if the maximum temperature allowed by the product is trespassed and when the sublimation drying is complete, thus providing a valuable tool for recipe design and optimization. Besides, the black box model can be applied to monitor the freeze-drying process: in this case, the measurement of product temperature is used as input variable of the neural network in order to provide in-line estimation of the state of the product (temperature and residual amount of ice). Various examples are presented and discussed, thus pointing out the strength of the too
Lower bounds on photometric redshift errors from Type Ia supernovae templates
Cosmology with Type Ia supernovae heretofore has required extensive
spectroscopic follow-up to establish a redshift. Though tolerable at the
present discovery rate, the next generation of ground-based all-sky survey
instruments will render this approach unsustainable. Photometry-based redshift
determination is a viable alternative, but introduces non-negligible errors
that ultimately degrade the ability to discriminate between competing
cosmologies. We present a strictly template-based photometric redshift
estimator and compute redshift reconstruction errors in the presence of
photometry and statistical errors. With reasonable assumptions for a cadence
and supernovae distribution, these redshift errors are combined with systematic
errors and propagated using the Fisher matrix formalism to derive lower bounds
on the joint errors in and relevant to the next
generation of ground-based all-sky survey.Comment: 23 pages, 6 figure
Linear system identification via backward-time observer models
Presented here is an algorithm to compute the Markov parameters of a backward-time observer for a backward-time model from experimental input and output data. The backward-time observer Markov parameters are decomposed to obtain the backward-time system Markov parameters (backward-time pulse response samples) for the backward-time system identification. The identified backward-time system Markov parameters are used in the Eigensystem Realization Algorithm to identify a backward-time state-space model, which can be easily converted to the usual forward-time representation. If one reverses time in the model to be identified, what were damped true system modes become modes with negative damping, growing as the reversed time increases. On the other hand, the noise modes in the identification still maintain the property that they are stable. The shift from positive damping to negative damping of the true system modes allows one to distinguish these modes from noise modes. Experimental results are given to illustrate when and to what extent this concept works
First-year Sloan Digital Sky Survey-II (SDSS-II) Supernova Results: Hubble Diagram and Cosmological Parameters
We present measurements of the Hubble diagram for 103 Type Ia supernovae
(SNe) with redshifts 0.04 < z < 0.42, discovered during the first season (Fall
2005) of the Sloan Digital Sky Survey-II (SDSS-II) Supernova Survey. These data
fill in the redshift "desert" between low- and high-redshift SN Ia surveys. We
combine the SDSS-II measurements with new distance estimates for published SN
data from the ESSENCE survey, the Supernova Legacy Survey, the Hubble Space
Telescope, and a compilation of nearby SN Ia measurements. Combining the SN
Hubble diagram with measurements of Baryon Acoustic Oscillations from the SDSS
Luminous Red Galaxy sample and with CMB temperature anisotropy measurements
from WMAP, we estimate the cosmological parameters w and Omega_M, assuming a
spatially flat cosmological model (FwCDM) with constant dark energy equation of
state parameter, w. For the FwCDM model and the combined sample of 288 SNe Ia,
we find w = -0.76 +- 0.07(stat) +- 0.11(syst), Omega_M = 0.306 +- 0.019(stat)
+- 0.023(syst) using MLCS2k2 and w = -0.96 +- 0.06(stat) +- 0.12(syst), Omega_M
= 0.265 +- 0.016(stat) +- 0.025(syst) using the SALT-II fitter. We trace the
discrepancy between these results to a difference in the rest-frame UV model
combined with a different luminosity correction from color variations; these
differences mostly affect the distance estimates for the SNLS and HST
supernovae. We present detailed discussions of systematic errors for both
light-curve methods and find that they both show data-model discrepancies in
rest-frame -band. For the SALT-II approach, we also see strong evidence for
redshift-dependence of the color-luminosity parameter (beta). Restricting the
analysis to the 136 SNe Ia in the Nearby+SDSS-II samples, we find much better
agreement between the two analysis methods but with larger uncertainties.Comment: Accepted for publication by ApJ
Testing Models of Intrinsic Brightness Variations in Type Ia Supernovae, and their Impact on Measuring Cosmological Parameters
For spectroscopically confirmed Type Ia supernovae we evaluate models of
intrinsic brightness variations with detailed data/Monte Carlo comparisons of
the dispersion in the following quantities: Hubble-diagram scatter, color
difference (B-V-c) between the true B-V color and the fitted color (c) from the
SALT-II light curve model, and photometric redshift residual. The data sample
includes 251 ugriz light curves from the 3-season Sloan Digital Sky Survey-II,
and 191 griz light curves from the Supernova Legacy Survey 3-year data release.
We find that the simplest model of a wavelength-independent (coherent) scatter
is not adequate, and that to describe the data the intrinsic scatter model must
have wavelength-dependent variations. We use Monte Carlo simulations to examine
the standard approach of adding a coherent scatter term in quadrature to the
distance-modulus uncertainty in order to bring the reduced chi2 to unity when
fitting a Hubble diagram. If the light curve fits include model uncertainties
with the correct wavelength dependence of the scatter, we find that the bias on
the dark energy equation of state parameter is negligible. However,
incorrect model uncertainties can lead to a significant bias on the distance
moduli, with up to ~0.05 mag redshift-dependent variation. For the recent SNLS3
cosmology results we estimate that this effect introduces an additional
systematic uncertainty on of ~0.02, well below the total uncertainty.
However, this uncertainty depends on the samples used, and thus this small
-uncertainty is not guaranteed in future cosmology results.Comment: accepted by Ap
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