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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS1976

On Schmidt's Vm estimator and other estimators of luminosity functions

Schmidt's (1968) estimator, sometimes used to calculate the luminosity function from a complete sample of observed objects, can be generalized naively to the case in which the maximum distance for detection is a function of the direction. Though unbiased, this estimator then does not have minimum variance and, in some cases, is inferior to the classical estimator. The classical estimator, however, is biased when the magnitude boxes are not infinitesimal. A generalization of Schmidt's estimator is proposed which is unbiased and usually superior to both Schmidt's and the classical estimator. Variance formulas and numerical examples are given. The results can be used in combining several catalogs.

Felten, J. E.↗
DOE OSTI2019

Temporal changes of seismic velocities in the San Jacinto Fault zone associated with the 2016 M w 5.2 Borrego Springs earthquake

We study temporal changes of seismic velocities associated with the 10 June 2016 Mw 5.2 Borrego Springs earthquake in the San Jacinto fault zone, using nine component Green's function estimates reconstructed from daily cross correlations of ambient noise. The analysed data are recorded by stations in two dense linear arrays, at Dry Wash (DW) and Jackass Flat (JF), crossing the fault surface trace ~3 km northwest and southeast of the event epicentre. The two arrays have 9 and 12 stations each with instrument spacing of 25–100 m. Relative velocity changes (δv/v) are estimated from arrival time changes in the daily correlation coda waveforms compared to a reference stack. The obtained array-average δv/v time-series exhibit changes associated with the Borrego Springs event, superposed with seasonal variations. The earthquake-related changes are characterized by a rapid coseismic velocity drop followed by a gradual recovery. This is consistently observed at both arrays using time- and frequency-domain δv/v analyses with data from different components in various frequency bands. Larger changes at lower frequencies imply that the variations are not limited to the near surface material. A decreasing coseismic velocity reduction with coda wave lapse time indicates larger coseismic structural perturbations in the fault zone and near-fault environment compared to the surrounding rock. Observed larger changes at the DW array compared to the JF array possibly reflect the northwestward rupture directivity of the Borrego Springs earthquake.

Geochemistry & Geophysics↗
DOE OSTI2018

Estimates for Functionals of Solutions to Higher-Order Heat-Type Equations with Random Initial Conditions

In the present paper we continue the investigation of solutions to higher-order heat-type equations with random initial conditions, which play the important role in many applied areas. We consider the random initial conditions given by harmonizable φ-sub-Gaussian processes. The main results are the bounds for the distributions of the suprema over bounded and unbounded domains for solutions of such equations. The results obtained in the paper hold, in particular, for the case of Gaussian initial condition.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2014

Signal/noise enhancement strategies for stochastically estimated correlation functions

We develop here strategies for enhancing the signal/noise ratio for stochastically sampled correlation functions. The techniques are general and offer a wide range of applicability. We demonstrate the potential of the approach with a generic two-state system and then explore the practical applicability of the method for single hadron correlators in lattice quantum chromodynamics. In the latter case, we determine the ground state energies of the pion, proton, and delta baryon, as well as the ground and first excited state energy of the rho meson using matrices of correlators computed on an exemplary ensemble of anisotropic gauge configurations. In the majority of cases, we find a modest reduction in the statistical uncertainties on extracted energies compared to conventional variational techniques. However, in the case of the delta baryon, we achieve a factor of 3 reduction in statistical uncertainties. The variety of outcomes achieved for single hadron correlators illustrates an inherent dependence of the method on the properties of the system under consideration and the operator basis from which the correlators are constructed.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1983

Error latency estimation using functional fault modeling

A complete modeling of faults at gate level for a fault tolerant computer is both infeasible and uneconomical. Functional fault modeling is an approach where units are characterized at an intermediate level and then combined to determine fault behavior. The applicability of functional fault modeling to the FTMP is studied. Using this model a forecast of error latency is made for some functional blocks. This approach is useful in representing larger sections of the hardware and aids in uncovering system level deficiencies.

Manthani, S. R.↗
NASA NTRS1986

Partial and Interaction Spline Models for the Semiparametric Estimation of Functions of Several Variables

A partial spline model is a model for a response as a function of several variables, which is the sum of a smooth function of several variables and a parametric function of the same plus possibly some other variables. Partial spline models in one and several variables, with direct and indirect data, with Gaussian errors and as an extension of GLIM to partially penalized GLIM models are described. Application to the modeling of change of regime in several variables is described. Interaction splines are introduced and described and their potential use for modeling non-linear interactions between variables by semiparametric methods is noted. Reference is made to recent work in efficient computational methods.

Wahba, Grace↗
DOE OSTI2021

Signal Preconditioning to Minimize Impulse Response Contribution

A study was performed to identify a method to minimize the effect of a linear time-invariant (LTI) system impulse response on an input. Three methods were studied: Wiener filter, the N4SID algorithm and transfer function estimation, the latter two using functions from MATLAB’s System Identification Toolbox. Although all three methods were able estimate an unknown forward impulse response given an input/output time series pair, only the Wiener filter was able to estimate a system inverse which satisfactorily solved the problem using a cosine similarity measure.

42 ENGINEERING↗
DOE OSTI2013

Effect of low and high storage temperatures on head space gas concentrations and physical properties of wood pellets

Headspace gas concentrations and wood pellet properties were studied in sealed glass canisters at 5–40 degrees C storage temperatures. CO2 and CO concentrations at 5, 10, 20 and 40 degrees C at the end of 23–28 days of storage were 1600 and 200, 4700 and 1200, and 31 200 and 15 800 parts per million by volume (ppmv) respectively. Corresponding O2 concentration was about 19•49, 19•20, 18•0 and 2•07% respectively. Non-linear regression equations adequately described the gas concentrations in the storage container as a function of time. Safe level estimation functions developed were linear for O2 and logarithmic for CO and CO2 concentrations. Measured pellet properties moisture, length, diameter, unit, bulk and tapped density, durability, calorific value, ash content and per cent fines were in the range of 4•6–5•02%, 14–15 mm, 6•4–6•5 mm, 1125–1175 kg m-3, 750–770 kg m-3, 825–840 kg m-3, 73–74%, 18•32–18•78 MJ kg-1, 0•65–0•74% and 0•13–0•15%. Durability values of pellets decreased by 13% at 40 degrees C storage temperature and other properties changed marginally.

09 BIOMASS FUELS↗
NASA NTRS1990

Interpolation schemes in the control of systems with unknown dynamics

Time-delay control for systems with unknown dynamics involves estimations. In its present form, these estimations include not only the interpolation of a time-varying function but also its derivative between data points. Presently, the control law is reformulated, taking into account the computation delay. A window-shifting scheme is then devised to view n data points as they are sampled in order to perform the function estimation. At each new sampling time, the window shifts to include the new data point as well as the last n-1 points. Several interpolation methods are considered. These methods use data points from a sampled function to construct a polynomial estimate of the function. The control system performance was experimentally tested using a servosystem. The results show that a Newtonian interpolation provided best results when the computation time was 0.4 times the sampling period.

Youcef-Toumi, K.↗
DOE OSTI2014

X-ray-selected galaxy groups in Boötes

We present the X-ray and optical properties of the galaxy groups selected in the Chandra X-Boötes survey. We used follow-up Chandra observations to better define the group sample and their X-ray properties. Group redshifts were measured from the AGN and Galaxy Evolution Survey spectroscopic data. We used photometric data from the NOAO Deep Wide Field Survey to estimate the group richness (N {sub gals}) and the optical luminosity (L {sub opt}). Our final sample comprises 32 systems at z < 1.75 with 14 below z = 0.35. For these 14 systems, we estimate velocity dispersions (σ {sub gr}) and perform a virial analysis to obtain the radii (R {sub 200} and R {sub 500}) and total masses (M {sub 200} and M {sub 500}) for groups with at least 5 galaxy members. We use the Chandra X-ray observations to derive the X-ray luminosity (L{sub X} ). We examine the performance of the group properties σ{sub gr}, L {sub opt}, and L{sub X} , as proxies for the group mass. Understanding how well these observables measure the total mass is important to estimate how precisely the cluster/group mass function is determined. Exploring the scaling relations built with the X-Boötes sample and comparing these with samples from the literature, we find a break in the L{sub X} -M {sub 500} relation at approximately M {sub 500} = 5 × 10{sup 13} M {sub ☉} (for M {sub 500} > 5 × 10{sup 13} M {sub ☉}, M{sub 500}∝L{sub X}{sup 0.61±0.02}, while for M {sub 500} ≤ 5 × 10{sup 13} M {sub ☉}, M{sub 500}∝L{sub X}{sup 0.44±0.05}). Thus, the mass-luminosity relation for galaxy groups cannot be described by the same power law as galaxy clusters. A possible explanation for this break is the dynamical friction, tidal interactions, and projection effects that reduce the velocity dispersion values of the galaxy groups. By extending the cluster luminosity function to the group regime, we predict the number of groups that new X-ray surveys, particularly eROSITA, will detect. Based on our cluster/group luminosity function estimates, eROSITA will identify ∼1800 groups (L{sub X} = 10{sup 41}-10{sup 43} erg s{sup –1}) within a distance of 200 Mpc. Since groups lie in large-scale filaments, this group sample will map the large-scale structure of the local universe.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2001

Aeroservoelastic Uncertainty Model Identification from Flight Data

Uncertainty modeling is a critical element in the estimation of robust stability margins for stability boundary prediction and robust flight control system development. There has been a serious deficiency to date in aeroservoelastic data analysis with attention to uncertainty modeling. Uncertainty can be estimated from flight data using both parametric and nonparametric identification techniques. The model validation problem addressed in this paper is to identify aeroservoelastic models with associated uncertainty structures from a limited amount of controlled excitation inputs over an extensive flight envelope. The challenge to this problem is to update analytical models from flight data estimates while also deriving non-conservative uncertainty descriptions consistent with the flight data. Multisine control surface command inputs and control system feedbacks are used as signals in a wavelet-based modal parameter estimation procedure for model updates. Transfer function estimates are incorporated in a robust minimax estimation scheme to get input-output parameters and error bounds consistent with the data and model structure. Uncertainty estimates derived from the data in this manner provide an appropriate and relevant representation for model development and robust stability analysis. This model-plus-uncertainty identification procedure is applied to aeroservoelastic flight data from the NASA Dryden Flight Research Center F-18 Systems Research Aircraft.

Brenner, Martin J.↗
NASA NTRS1981

Statistics of some atmospheric turbulence records relevant to aircraft response calculations

Methods for characterizing atmospheric turbulence are described. The methods illustrated include maximum likelihood estimation of the integral scale and intensity of records obeying the von Karman transverse power spectral form, constrained least-squares estimation of the parameters of a parametric representation of autocorrelation functions, estimation of the power spectra density of the instantaneous variance of a record with temporally fluctuating variance, and estimation of the probability density functions of various turbulence components. Descriptions of the computer programs used in the computations are given, and a full listing of these programs is included.

Mark, W. D.↗
NASA NTRS1975

A sequential nonparametric pattern classification algorithm based on the Wald SPRT

A sequential nonparametric pattern classification procedure is presented. The method presented is an estimated version of the Wald sequential probability ratio test (SPRT). This method utilizes density function estimates, and the density estimate used is discussed, including a proof of convergence in probability of the estimate to the true density function. The classification procedure proposed makes use of the theory of order statistics, and estimates of the probabilities of misclassification are given. The procedure was tested on discriminating between two classes of Gaussian samples and on discriminating between two kinds of electroencephalogram (EEG) responses.

Poage, J. L.↗