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At least 289 records · Page 16

All sources NASA NTRS DOE OSTI

NASA NTRS1988

Expert system prototype developments for NASA-KSC business and engineering applications

Prototype expert systems developed for a variety of NASA projects in the business/management and engineering domains are discussed. Business-related problems addressed include an assistant for simulating launch vehicle processing, a plan advisor for the acquisition of automated data processing equipment, and an expert system for the identification of customer requirements. Engineering problems treated include an expert system for detecting potential ignition sources in LOX and gaseous-oxygen transportation systems and an expert system for hazardous-gas detection.

Ragusa, James M.↗

NASA NTRS1992

Laboratory for Engineering Man/Machine Systems (LEMS): System identification, model reduction and deconvolution filtering using Fourier based modulating signals and high order statistics

Several important problems in the fields of signal processing and model identification, such as system structure identification, frequency response determination, high order model reduction, high resolution frequency analysis, deconvolution filtering, and etc. Each of these topics involves a wide range of applications and has received considerable attention. Using the Fourier based sinusoidal modulating signals, it is shown that a discrete autoregressive model can be constructed for the least squares identification of continuous systems. Some identification algorithms are presented for both SISO and MIMO systems frequency response determination using only transient data. Also, several new schemes for model reduction were developed. Based upon the complex sinusoidal modulating signals, a parametric least squares algorithm for high resolution frequency estimation is proposed. Numerical examples show that the proposed algorithm gives better performance than the usual. Also, the problem was studied of deconvolution and parameter identification of a general noncausal nonminimum phase ARMA system driven by non-Gaussian stationary random processes. Algorithms are introduced for inverse cumulant estimation, both in the frequency domain via the FFT algorithms and in the domain via the least squares algorithm.

Pan, Jianqiang↗

NASA NTRS1978

Identification of the stability parameters of an aeroelastic airplane

Phase variable transformations are used to construct the mathematical model of an aeroelastic aircraft in a form that is amenable to partial or piecemeal acceptance of parameters estimated from flight data. The problem is one of parameter identification of large scale dynamic systems involving a system matrix characterized by about 200 elements. A mathematical model of the USAF Total In-Flight Simulator was computed using the FLEXSTAB program. As data became available during the progress of the flight test program, it was processed and substituted in the mathematical model for parameters obtained from the FLEXSTAB program. Results tend to show a progressive and orderly transition from an analytically defined mathematical model to one obtained from the flight tests of the actual aircraft.

Rynaski, E. G.↗

NASA NTRS1979

Manual LANDSAT data analysis for crop type identification

The process of manual identification of crop type by human analysts and problems associated in LACIE that were associated with manual crop identification measurement procedures are described. Research undertaken in cooperation with LACIE operations by the supporting research community to effect solutions to, or obtain greater understanding of the problems is discussed.

Hay, C. M.↗

NASA NTRS1985

Identification of dynamic systems, theory and formulation

The problem of estimating parameters of dynamic systems is addressed in order to present the theoretical basis of system identification and parameter estimation in a manner that is complete and rigorous, yet understandable with minimal prerequisites. Maximum likelihood and related estimators are highlighted. The approach used requires familiarity with calculus, linear algebra, and probability, but does not require knowledge of stochastic processes or functional analysis. The treatment emphasizes unification of the various areas in estimation in dynamic systems is treated as a direct outgrowth of the static system theory. Topics covered include basic concepts and definitions; numerical optimization methods; probability; statistical estimators; estimation in static systems; stochastic processes; state estimation in dynamic systems; output error, filter error, and equation error methods of parameter estimation in dynamic systems, and the accuracy of the estimates.

Maine, R. E.↗

NASA NTRS1972

Identification of rotating assembly inertial and bearing parameters.

The problem of estimating the state of a turbopump rotating assembly and identifying unknown products of inertia and unknown bearing parameters is considered. A linearized, extended Kalman filtering approach has been used and found to be successful for both state estimation and parameter identification in an inherently nonlinear problem. The input data were simulated Bently test data obtained from a verified 12-dimensional state-space model and were compared with real Bently test data obtained from NASA. The results prove the feasibility of using this model and this type of test data to obtain the hidden parameters of a typical turbopump rotating assembly.

Alspach, D. L.↗

NASA NTRS1995

Calibration of Tests for Time-Dilation in GRB Pulse Structures

If gamma-ray bursts are at cosmological distances, then not only their constituent pulses but also the intervals between pulses should be time-dilated. Unlike time-dilation measures of pulse emission, intervals would appear to require less "K-correction" for redshift of narrower temporal structure from higher energy into the band of observation. However, stretching of pulse intervals is intrinsically difficult to measure without incurring a timescale-dependent bias since, as time profiles are stretched, more structure can appear at the limit of resolution. This problem is compounded in dimmer bursts since identification of significant structures is problematic. We have attempted to minimize brightness bias problems by equalizing the signal-to-noise level of all bursts. Then, analyzing wavelet-denoised profiles binned to several resolutions, we identify significant (3 - 4 sigma) fluctuations between pulse structures and interjacent valleys. When bursts are ranked by peak flux, an interval time-dilation signature is evident, but its measure is dependent upon choice of temporal resolution. This result appears to tentatively and qualitatively confirm the work of Davis, who found such a signature using a pulse fitting approach.

Norris, J. P.↗

NASA NTRS1981

Stochastic models of cover class dynamics

Investigations related to satellite remote sensing of vegetation have been concerned with questions of signature identification and extension, cover inventory accuracy, and change detection and monitoring. Attention is given to models of ecological succession, present directions in successional modeling and analysis, nondynamic spatial models, issues in the analysis of spatial data, and aspects of spatial modeling. Issues in time-series analysis are considered along with dynamic spatial models, and problems of model specification and identification.

Barringer, T. H.↗

DOE OSTI2021

An optimization-based approach to parameter learning for fractional type nonlocal models

Nonlocal operators of fractional type are a popular modeling choice for applications that do not adhere to classical diffusive behavior; however, one major challenge in nonlocal simulations is the selection of model parameters. In this study we propose an optimization-based approach to parameter identification for fractional models with an optional truncation radius. We formulate the inference problem as an optimal control problem where the objective is to minimize the discrepancy between observed data and an approximate solution of the model, and the control variables are the fractional order and the truncation length. For the numerical solution of the minimization problem we propose a gradient-based approach, where we enhance the numerical performance by an approximation of the bilinear form of the state equation and its derivative with respect to the fractional order. Several numerical tests in one and two dimensions illustrate the theoretical results and show the robustness and applicability of our method.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

An optimization-based approach to parameter learning for fractional type nonlocal models

Nonlocal operators of fractional type are a popular modeling choice for applications that do not adhere to classical diffusive behavior; however, one major challenge in nonlocal simulations is the selection of model parameters. In this work we propose an optimization-based approach to parameter identification for fractional models with an optional truncation radius. We formulate the inference problem as an optimal control problem where the objective is to minimize the discrepancy between observed data and an approximate solution of the model, and the control variables are the fractional order and the truncation length. For the numerical solution of the minimization problem we propose a gradient-based approach, where we enhance the numerical performance by an approximation of the bilinear form of the state equation and its derivative with respect to the fractional order. Several numerical tests in one and two dimensions illustrate the theoretical results and show the robustness and applicability of our method.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1985

Modal vector estimation for closely-spaced-frequency modes

Identification of modal parameters is made more difficult if the system has repeated frequencies or even closely spaced frequencies. This paper discusses the problems associated with closely-spaced-frequency modes and describes two related methods for dealing with these problems. It is demonstrated that proper identification of the system parameters of such systems requires information from multiple inputs and/or multiple outputs. The analytical basis for the 'minimum coincident response method' is outlined, and examples of the use of these two techniques to extract model parameters are presented.

Craig, R. R., Jr.↗

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 NTRS1987

Approximation techniques for domain identification in two-dimensional parabolic systems under boundary observations

The identification of the geometrical structure of the system boundary for a two-dimensional diffusion system is considered. The domain identification treated is converted into an optimization problem based on a fit-to-data criterion and theoretical convergence results for approximate identification techniques are discussed. Results of numerical experiments to demonstrate the efficiency of the theoretical ideas are reported.

Banks, H. T.↗

NASA NTRS1986

Spline-based distributed system identification with application to large space antennas

A parameter and state estimation technique for distributed models is demonstrated through the solution of a problem generic to large space antenna system identification. Assuming the position of the reflective surface of the maypole (hoop/column) antenna to be approximated by the static two-dimensional, stretched-membrane partial differential equation with variable-stiffness coefficient functions, a spline-based approximation procedure is described that estimates the shape and stiffness functions from data set observations. For given stiffness functions, the Galerkin projection with linear spline-based functions is applied to project the distributed problem onto a finite-dimensional subspace wherein algebraic equations exist for determining a static shape (state) prediction. The stiffness functions are then parameterized by cubic splines and the parameters estimated by an output error technique. Numerical results are presented for data descriptive of a 100-m-diameter maypole antenna.

Banks, H. T.↗

DOE OSTI2021

Physics-Informed Neural Network Method for Forward and Backward Advection-Dispersion Equations

Advection-dispersion equations (ADEs) are commonly used to describe transport phenomena in porous media. Even though mature discretization-based numerical methods for ADEs exist, some challenges still remain, especially when it comes to solving advection-dominated forward ADEs and diffusion-dominated backward ADEs. The latter problem usually arises in the source identification context and leads to numerically unstable grid-based solutions that require a form of regularization or should be treated as an inverse problem that is computationally more expensive because it requires solving the forward problem multiple times. In this study, we propose a discretization-free approach based on the physics-informed neural network (PINN) method for solving coupled ADE and Darcy flow equations with space-dependent hydraulic conductivity. In this approach, the hydraulic conductivity, hydraulic head, and concentration fields are approximated with deep neural networks (DNNs). We assume that the conductivity field is given by its values on a grid, and we use these values to train the conductivity DNN. The head and concentration DNNs are trained by minimizing the residuals of the flow equation and ADE and using the initial and boundary conditions as additional constraints. The PINN method is applied to one- and two-dimensional forward ADE problems, where its performance for various P\'{e}clet numbers ($Pe$) is compared with the analytical and numerical solutions. We find that the PINN method is accurate with errors of less than 1\% and outperforms some conventional discretization-based methods for $Pe$ larger that 100. Next, we demonstrate that the PINN method remains accurate for the backward ADEs, with the relative errors in most cases staying under 5\% compared to the reference concentration field. Finally, we show that when available, the concentration measurements can be easily incorporated in the PINN method and significantly improve (by more than 50\% in the considered cases) the accuracy of the PINN solution of the backward ADE.

He, Qizhi↗

NASA NTRS1992

Using partially labeled data for normal mixture identification with application to class definition

The problem of estimating the parameters of a normal mixture density when, in addition to the unlabeled samples, sets of partially labeled samples are available is addressed. The density of the multidimensional feature space is modeled with a normal mixture. It is assumed that the set of components of the mixture can be partitioned into several classes and that training samples are available from each class. Since for any training sample the class of origin is known but the exact component of origin within the corresponding class is unknown, the training samples as considered to be partially labeled. The EM iterative equations are derived for estimating the parameters of the normal mixture in the presence of partially labeled samples. These equations can be used to combine the supervised and nonsupervised learning processes.

Shahshahani, Behzad M.↗

NASA NTRS1990

Identification challenges for large space structures

The paper examines the on-orbit modal identification of large space structures, stressing the importance of planning and experience, in preparation for the Space Station Structural Characterization Experiment (SSSCE) for the Space Station Freedom. The necessary information to foresee and overcome practical difficulties is considered in connection with seven key factors, including test objectives, dynamic complexity of the structure, data quality, extent of exploratory studies, availability and understanding of software tools, experience with similar problems, and pretest analytical conditions. These factors affect identification success in ground tests. Comparisons with similar ground tests of assembled systems are discussed, showing that the constraints of space tests make these factors more significant. The absence of data and experiences relating to on-orbit modal identification testing is shown to make identification a uniquely mathematical problem, although all spacecraft are constructed and verified by proven engineering methods.

Pappa, Richard S.↗

DOE OSTI2022

Joint Survey Processing. I. Compact Oddballs in the COSMOS Field—Low-luminosity Quasars at z > 6?

The faint-end slope of the quasar luminosity function at z ~ 6 and its implication on the role of quasars in reionizing the intergalactic medium at early times has been an outstanding problem for some time. The identification of faint high-redshift quasars with luminosities of <10 44.5 erg s –1 is challenging. They are rare (few per square degree), and the separation of these unresolved quasars from late-type stars and compact star-forming galaxies is difficult from ground-based observations alone. In addition, source confusion becomes significant at >25 mag, with ~30% of sources having their flux contaminated by foreground objects when the seeing resolution is ~0''.7. We mitigate these issues by performing a pixel-level joint processing of ground and space-based data from Subaru/Hyper-SuprimeCam (HSC) and Hubble Space Telescope (HST) Advanced Camera for Surveys (ACS). We create a deconfused catalog over the 1.64 deg 2 of the COSMOS field, after accounting for spatial varying point-spread functions and astrometric differences between the two data sets. We identify twelve low-luminosity (M UV ~ –21 mag) z > 6 quasar candidates through (i) their red color measured between ACS/F814W and HSC/i band and (ii) their compactness in the space-based data. Nondetections of our candidates in Hubble DASH data argues against contamination from late-type stars. Our constraints on the faint end of the quasar luminosity function at z ~ 6.4 suggest a negligibly small contribution to reionization compared to the star-forming galaxy population. The confirmation of our candidates and the evolution of number density with redshift could provide better insights into how supermassive galaxies grew in the first billion years of cosmic time.

79 ASTRONOMY AND ASTROPHYSICS↗