A Massively Parallel Bayesian Approach to Planetary Protection Trajectory Analysis and Design
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parallel computing↗
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Kepler Data Validation Time Series File: Description of File Format and Content
The Kepler space mission searches its time series data for periodic, transit-like signatures. The ephemerides of these events, called Threshold Crossing Events (TCEs), are reported in the TCE tables at the NASA Exoplanet Archive (NExScI). Those TCEs are then further evaluated to create planet candidates and populate the Kepler Objects of Interest (KOI) table, also hosted at the Exoplanet Archive. The search, evaluation and export of TCEs is performed by two pipeline modules, TPS (Transit Planet Search) and DV (Data Validation). TPS searches for the strongest, believable signal and then sends that information to DV to fit a transit model, compute various statistics, and remove the transit events so that the light curve can be searched for other TCEs. More on how this search is done and on the creation of the TCE table can be found in Tenenbaum et al. (2012), Seader et al. (2015), Jenkins (2002). For each star with at least one TCE, the pipeline exports a file that contains the light curves used by TPS and DV to find and evaluate the TCE(s). This document describes the content of these DV time series files, and this introduction provides a bit of context for how the data in these files are used by the pipeline.
DV Time Series↗
Introduction to Big Earth Data Applications
Climate and weather modeling generate enormous volumes that make iterative analysis challenging, spurring the development of new ways to work with the data. A theme going across applications is the need to identify and highlight "interesting" data for the scientist to focus on. Operational applications often scale up from small, local studies to larger spatial scales with more analysis targets.
parallel processing (computers)↗
Variational Coupled Loads Analysis using the Hybrid Parametric Variation Method
Time-domain coupled loads analysis (CLA)is used to determine the response of a launch vehicle and payload system to transient forces, such as liftoff, engine ignitions and shutdowns, jettison events, and atmospheric flight loads, such as buffet. CLA, using Hurty/Craig-Bampton (HCB)component models, is the accepted method for the establishment of design-level loads for launch systems. However, uncertainty in the component models flows into uncertainty in predicted system results. Uncertainty in the structural responses during launch is a significant concern because small variations in launch vehicle and payload mode shapes and their interactions can result in significant variations in system loads. Uncertainty quantification (UQ)is used to determine statistical bounds on prediction accuracy based on model uncertainty. In this paper uncertainty is treated at the HCB component-model level. In an effort to account for model uncertainties and statistically bound their effect on CLA predictions, this work combines CLA with UQ in a process termed variational coupled loads analysis (VCLA). The modeling of uncertainty using a parametric approach, in which input parameters are represented by random variables, is common, but its major drawback is the resulting uncertainty is limited to the form of the nominal model. Uncertainty in model form is one of the biggest contributors to uncertainty in complex built-up structures. Model-form uncertainty can be represented using a nonparametric approach based on random matrix theory (RMT). In this work, UQ is performed using the hybrid parametric variation (HPV)method, which combines parametric with nonparametric uncertainty at the HCB component model level. The HPV method requires the selection of dispersion values for the HCB fixed-interface (FI)eigenvalues, and the HCB mass and stiffness matrices. The dispersions are based upon component test-analysis modal correlation results. During VCLA, random component models are assembled into an ensemble of random systems using a Monte Carlo (MC)approach. CLA is applied to each of the ensemble members to produce an ensemble of system-level responses for statistical analysis. The proposed methodology is demonstrated through its application to a buffet loads analysis of NASA’s Space Launch System (SLS)during the transonic regime fifty seconds after liftoff. Core stage (CS)section shears and moments are recovered, and statistics are computed.
Uncertainty Quantification↗
Deep Interacting Multiple Model Filtering
In this paper, a deep learning-based multiple model estimation framework is presented for the state estimation of hybrid dynamical systems from high dimensional observations such as camera images. A low dimensional vector which represents the measurement of the latent dynamical system and its corresponding variance are learned using a deep encoder neural network. An Interacting Multiple Model (IMM) filter is used to generate the latent state estimates and covariances using multiple dynamical models, which can be learned using backpropagation through time. The state estimates of the dynamical system and the corresponding covariance matrix are generated from the latent state estimates and covariance using a deep decoder neural network. The whole network is trained in an end-to-end manner using a loss function which minimizes the negative log-likelihood of the neural network parameters. Simulation results are presented using a 2D bouncing ball example and estimation error statistics are computed which demonstrates the accuracy and consistency of the estimation.
Ghananeel Rotithor↗
Evaluating Statistical Process Control (SPC) techniques and computing the uncertainty of force calibrations
In recent years there has been a push within NASA to use statistical techniques to improve the quality of production. Two areas where statistics are used are in establishing product and process quality control of flight hardware and in evaluating the uncertainty of calibration of instruments. The Flight Systems Quality Engineering branch is responsible for developing and assuring the quality of all flight hardware; the statistical process control methods employed are reviewed and evaluated. The Measurement Standards and Calibration Laboratory performs the calibration of all instruments used on-site at JSC as well as those used by all off-site contractors. These calibrations must be performed in such a way as to be traceable to national standards maintained by the National Institute of Standards and Technology, and they must meet a four-to-one ratio of the instrument specifications to calibrating standard uncertainty. In some instances this ratio is not met, and in these cases it is desirable to compute the exact uncertainty of the calibration and determine ways of reducing it. A particular example where this problem is encountered is with a machine which does automatic calibrations of force. The process of force calibration using the United Force Machine is described in detail. The sources of error are identified and quantified when possible. Suggestions for improvement are made.
Navard, Sharon E.↗
Measurements of the stochastic nature of atmospheric spectral amplitudes
The stochastic nature of the power spectral amplitudes of the neutral atmospheric boundary layer is examined. Probability density distributions and probability distributions of longitudinal and lateral power spectra amplitudes are computed from neutral atmospheric boundary layers. The statistical distributions are computed for frequencies of 0.006, 0.01, 0.03, 0.06, 0.1, and 0.5 Hz at each of the elevations of 18, 30, 60, 90, 120, and 150 m. When the probability density distributions are properly nondimensionalized, the data tend to collapse to a universal curve. An empirical curve fit to the universal nondimensionalized probability density distribution is also given. Probability distributions of individual frequency power spectral amplitudes are also presented for all elevations and frequencies. An interesting observation from the data is that greater than 10 percent of the time the power spectral amplitude at a given frequency will genrally be more than three times the temporal mean value computed by standard Fourier techniques. The standard power spectral density curves are also included in the report.
Cliff, W. C.↗
Computational Bayesian Methods Applied to Complex Problems in Bio and Astro Statistics
In this dissertation we apply computational Bayesian methods to three distinct problems. In the first chapter, we address the issue of unrealistic covariance matrices used to estimate collision probabilities. We model covariance matrices with a Bayesian Normal-Inverse-Wishart model, which we fit with Gibbs sampling. In the second chapter, we are interested in determining the sample sizes necessary to achieve a particular interval width and establish non-inferiority in the analysis of prevalences using two fallible tests. To this end, we use a third order asymptotic approximation. In the third chapter, we wish to synthesize evidence across multiple domains in measurements taken longitudinally across time, featuring a substantial amount of structurally missing data, and fit the model with Hamiltonian Monte Carlo in a simulation to analyze how estimates of a parameter of interest change across sample sizes.
Elrod, Chris↗
Computer program for prediction of fuel consumption statistical data for an upper stage three-axes stabilized on-off control system
A FORTRAN coded computer program and method to predict the reaction control fuel consumption statistics for a three axis stabilized rocket vehicle upper stage is described. A Monte Carlo approach is used which is more efficient by using closed form estimates of impulses. The effects of rocket motor thrust misalignment, static unbalance, aerodynamic disturbances, and deviations in trajectory, mass properties and control system characteristics are included. This routine can be applied to many types of on-off reaction controlled vehicles. The pseudorandom number generation and statistical analyses subroutines including the output histograms can be used for other Monte Carlo analyses problems.
Source record↗
Changing the Paradigm: Preparing Students for the Computing Profession in the 21st Century
The dramatic technological developments of the past decade have led to a tremendous growth in the demand for computer science professionals well-versed in advanced technology and techniques. NASA, traditionally a haven for cutting-edge innovators, is now competing with every industrial and government sector for computer science talent. The computer science program at University of Texas at San Antonio (UTSA) faces challenges beyond those intrinsically presented by rapid technological change, because a significant number of UTSA students come from low-income families with no Internet or computer access at home. An examination of enrollment statistics for the computer science program at UTSA showed that very few students who entered as freshmen successfully graduated. The upper division courses appeared to be populated by graduate students removing deficiencies and by transfer students. The faculty was also concerned that the students who did graduate from the program did not have the strong technical and programming skills that the CS program had been noted for in the community during the 1980's.
Robbins, Kay A.↗
Statistical properties of filtered pseudorandom digital sequences formed from the sum of maximum-length sequences
The statistics of filtered pseudorandom digital sequences called hybrid-sum sequences, formed from the modulo-two sum of several maximum-length sequences, are analyzed. The results indicate that a relation exists between the statistics of the filtered sequence and the characteristic polynomials of the component maximum length sequences. An analysis procedure is developed for identifying a large group of sequences with good statistical properties for applications requiring the generation of analog pseudorandom noise. By use of the analysis approach, the filtering process is approximated by the convolution of the sequence with a sum of unit step functions. A parameter reflecting the overall statistical properties of filtered pseudorandom sequences is derived. This parameter is called the statistical quality factor. A computer algorithm to calculate the statistical quality factor for the filtered sequences is presented, and the results for two examples of sequence combinations are included. The analysis reveals that the statistics of the signals generated with the hybrid-sum generator are potentially superior to the statistics of signals generated with maximum-length generators. Furthermore, fewer calculations are required to evaluate the statistics of a large group of hybrid-sum generators than are required to evaluate the statistics of the same size group of approximately equivalent maximum-length sequences.
Wallace, G. R.↗
Statistical properties of solar granulation derived from the SOUP instrument on Spacelab 2
Computer algorithms and statistical techniques were used to identify, measure, and quantify the properties of solar granulation derived from movies collected by the Solar Optical Universal Polarimeter on Spacelab 2. The results show that there is neither a typical solar granule nor a typical granule evolution. A granule's evolution is dependent on local magnetic flux density, its position with respect to the active region plage, its position in the mesogranulation pattern, and the evolution of granules in its immediate neighborhood.
Title, A. M.↗
Study of statistical characteristics of unsteady pressure Final report, Aug. 30, 1966 - Sep. 30, 1967
Digital computer program for statistical analysis of wind tunnel test data taken on Saturn-class vehicle models
Dennis, G. O.↗
Simulation of the pressure field near a jet by randomly distributed vortex rings
Fluctuations of the pressure field in the vicinity of a jet are simulated numerically by a flow model consisting of axially symmetric vortex rings with viscous cores submerged in a uniform stream. The time interval between the shedding of successive vortices is taken to be a random variable with a probability distribution chosen to match that from experiments. It is found that up to 5 diameters downstream of the jet exit, statistics of the computed pressure field are in good agreement with experimental results. Statistical comparisons are provided for the overall sound pressure level, the peak amplitude, and the Strouhal number based on the peak frequency of the pressure signals.
Fung, Y. T.↗
Statistical Analysis of IR Spectra - Stanford Programs Applied to USGS Spectra in Tech Letter Number 13
Computer program for statistical analysis of infrared emittance spectra of rock samples
Source record↗
Statistical parameters of reverberating fields, calculated by computer
A method, developed for making measurements in reverberation chambers in the steady state mode, is reported. Spatial distribution patterns of the square of the sound pressure vs type of exciting signal were produced theoretically and checked experimentally in a reverberation chamber. Results of measurements of spatial irregularities of the field with pure tones and with noise bands in the 200 to 2,000 Hz frequency range were used for calculating statistical parameters of the reverbation fields by computer. The calculation program included determination of density probability function, integral distribution pattern, mathematical expectation, normalized dispersion of the mean square of the pressure.
Lebedeva, I. V.↗
An empirical analysis of the distribution of the duration of overshoots in a stationary gaussian stochastic process
This analysis utilizes computer simulation and statistical estimation. Realizations of stationary gaussian stochastic processes with selected autocorrelation functions are computer simulated. Analysis of the simulated data revealed that the mean and the variance of a process were functionally dependent upon the autocorrelation parameter and crossing level. Using predicted values for the mean and standard deviation, by the method of moments, the distribution parameters was estimated. Thus, given the autocorrelation parameter, crossing level, mean, and standard deviation of a process, the probability of exceeding the crossing level for a particular length of time was calculated.
Parrish, R. S.↗
DDP-116 general digital filtering
The methods are described for calibrating, selecting filter weights, filtering, and computing filter response functions. These methods are computed on a statistical analyzer (STAN) system with a Honeywell DDP-116 central processor. The following filter types are computed: all pass, low pass, high pass, band pass, band rejection, and derivative.
Jones, J. A.↗