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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 NTRS1988

The effects of feedback lighting on the circadian drinking rhythm in the diurnal new world primate Saimiri sciureus

Feedback lighting provides illumination primarily during the subjective night (i.e., the photosensitive portion of the circadian cycle) in response to a given behavior. This technique has previously been used to test the nonparametric model of entrainment in nocturnal rodents. In three species (Rattus norvegicus, Mesocricetus auratus, and Mus musculus), the free-running period of the locomotor activity rhythm was similar whether the animals were exposed to continuous light or discrete light pulses occurring essentially only during the subjective night (i.e., feedback lighting). In the current experiments, feedback lighting was presented to squirrel monkeys so that light fell predominantly during the subjective night. Feedback lighting was linked to the drinking behavior in this diurnal primate so that when the animal drank, the lights went out. Despite the seemingly adverse predicament, the monkeys maintained regular circadian drinking rhythms. Furthermore, just as the period of the free-running activity rhythms of nocturnal rodents exposed to continuous light or feedback lighting were similar, the period of the drinking rhythms of the squirrel monkeys in continuous light and feedback lighting were comparable (25.6 +/- 0.1 and 25.9 +/- 0.1 hours, respectively), despite a substantial decrease in the total amount of light exposure associated with feedback lighting. The free-running period of monkeys exposed to continuous dark (24.5 +/- 0.1 hours) was significantly shorter than either of the two lighting conditions (P < 0.001). The results presented for the drinking rhythm were confirmed by examination of the temperature and activity rhythms. Therefore, discrete light pulses given predominately during the subjective night are capable of simulating the effects of continuous light on the free-running period of the circadian rhythms of a diurnal primate. The response of squirrel monkeys to feedback lighting thus lends further support for the model and suggests that the major entrainment mechanisms are similar in nocturnal rodents and diurnal primates.

NASA Discipline Number 40-30↗

NASA NTRS2001

Absence of diurnal variation of C-reactive protein concentrations in healthy human subjects

BACKGROUND: The concentration of C-reactive protein (CRP) in otherwise healthy subjects has been shown to predict future risk of myocardial infarction and stroke. CRP is synthesized by the liver in response to interleukin-6, the serum concentration of which is subject to diurnal variation. METHODS: To examine the existence of a time-of-day effect for baseline CRP values, we determined CRP concentrations in hourly blood samples drawn from healthy subjects (10 males, 3 females; age range, 21-35 years) during a baseline day in a controlled environment (8 h of nighttime sleep). RESULTS: Overall CRP concentrations were low, with only three subjects having CRP concentrations >2 mg/L. Comparison of raw data showed stability of CRP concentrations throughout the 24 h studied. When compared with cutoff values of CRP quintile derived from population-based studies, misclassification of greater than one quintile did not occur as a result of diurnal variation in any of the subjects studied. Nonparametric ANOVA comparing different time points showed no significant differences for both raw and z-transformed data. Analysis for rhythmic diurnal variation using a method fitting a cosine curve to the group data was negative. CONCLUSIONS: Our data show that baseline CRP concentrations are not subject to time-of-day variation and thus help to explain why CRP concentrations are a better predictor of vascular risk than interleukin-6. Determination of CRP for cardiovascular risk prediction may be performed without concern for diurnal variation.

Non-NASA Center↗

NASA NTRS2006

Shuttle Debris Impact Tool Assessment Using the Modern Design of Experiments

Computational tools have been developed to estimate thermal and mechanical reentry loads experienced by the Space Shuttle Orbiter as the result of cavities in the Thermal Protection System (TPS). Such cavities can be caused by impact from ice or insulating foam debris shed from the External Tank (ET) on liftoff. The reentry loads depend on cavity geometry and certain Shuttle state variables, among other factors. Certain simplifying assumptions have been made in the tool development about the cavity geometry variables. For example, the cavities are all modeled as shoeboxes , with rectangular cross-sections and planar walls. So an actual cavity is typically approximated with an idealized cavity described in terms of its length, width, and depth, as well as its entry angle, exit angle, and side angles (assumed to be the same for both sides). As part of a comprehensive assessment of the uncertainty in reentry loads estimated by the debris impact assessment tools, an effort has been initiated to quantify the component of the uncertainty that is due to imperfect geometry specifications for the debris impact cavities. The approach is to compute predicted loads for a set of geometry factor combinations sufficient to develop polynomial approximations to the complex, nonparametric underlying computational models. Such polynomial models are continuous and feature estimable, continuous derivatives, conditions that facilitate the propagation of independent variable errors. As an additional benefit, once the polynomial models have been developed, they require fewer computational resources to execute than the underlying finite element and computational fluid dynamics codes, and can generate reentry loads estimates in significantly less time. This provides a practical screening capability, in which a large number of debris impact cavities can be quickly classified either as harmless, or subject to additional analysis with the more comprehensive underlying computational tools. The polynomial models also provide useful insights into the sensitivity of reentry loads to various cavity geometry variables, and reveal complex interactions among those variables that indicate how the sensitivity of one variable depends on the level of one or more other variables. For example, the effect of cavity length on certain reentry loads depends on the depth of the cavity. Such interactions are clearly displayed in the polynomial response models.

DeLoach, R.↗

NASA NTRS2007

Contrasting Perspectives Of Junior versus Senior NASA ISS Flight Controllers On Leadership And Cultural Issues

NASA flight controllers have always worked in a very demanding environment, but the International Space Station (ISS) poses even more challenges than prior missions. A recent NASA/Ames survey by Parke and Orasanu of NASA/Johnson flight controllers uncovered concerns about communications problems between American personnel and their international counterparts. To better understand these problems, we interviewed 14 senior and 12 junior ISS flight controllers at NASA/Johnson about leadership and cultural challenges they face and strategies for addressing these challenges. The qualitative interview data were coded and tabulated. Here we present quantitative analyses testing for differences between junior and senior controllers. Based on nonparametric statistical tests comparing responses across groups, the senior controllers were significantly more aware of the impact of working in dispersed teams, the context of constant change, and the upcoming multilateral challenges, while junior controllers were more aware of language and cultural issues. We consider our findings in light of other studies of controllers and other known differences between senior and junior controllers. For example, the fact that senior controllers had their formative early experience controlling pre-ISS short-duration Shuttle missions seems to have both positive and negative aspects, which are supported by our data. Our findings may also reflect gender differences, but we cannot unconfound this effect in our data because all the senior respondents were males. Many of the junior-senior differences are not only due to elapsed time on the job, but also due to a cohort effect. The findings of this study should be used for training curricula tailored differently for junior and senior controllers.

Clement, James L.↗

NASA NTRS2008

Comparing Local Starbursts to High-Redshift Galaxies: A Search for Lyman-Break Analogs

We compare the restframe far-ultraviolet (FUV) morphologies of 8 nearby interacting and starburst galaxies (Arp 269, M 82, Mrk 08, NGC 0520, NGC 1068, NGC 3079, NGC 3310, NGC 7673) with 54 galaxies at z approx.1.5 and 46 galaxies at z approx.4 in the Great Observatories Origins Deep Survey (GOODS) images taken with the Advanced Camera for Surveys onboard the Hubble Space Telescope. We calculate the Gini coefficient (G), the second order moment of 20% of the brightest pixels (M20), and the S ersic index (n). We find that 20% (11/54) of z approx.1.5 and 37% (17/46) of z approx.4 galaxies are bulge-like, using G and M20. We also find approx.70% of the z approx.1.5 and z approx.4 galaxies have exponential disks with n > 0.8. The 2D profile combined with the nonparametric methods provides more detail, concerning the nature of disturbed systems, such as merger and post-merger types. We also provide qualitative descriptions of each galaxy system and at each redshift. We conclude that Mrk 08, NGC 3079, and NGC 7673 have similar morphologies as the starburst FUV restframe galaxies and Lyman-break galaxies at z approx.1.5 and 4, and determine that they are Lyman-break analogs.

Petty, Sara M.↗

NASA NTRS2010

Model Robust Calibration: Method and Application to Electronically-Scanned Pressure Transducers

This article presents the application of a recently developed statistical regression method to the controlled instrument calibration problem. The statistical method of Model Robust Regression (MRR), developed by Mays, Birch, and Starnes, is shown to improve instrument calibration by reducing the reliance of the calibration on a predetermined parametric (e.g. polynomial, exponential, logarithmic) model. This is accomplished by allowing fits from the predetermined parametric model to be augmented by a certain portion of a fit to the residuals from the initial regression using a nonparametric (locally parametric) regression technique. The method is demonstrated for the absolute scale calibration of silicon-based pressure transducers.

Walker, Eric L.↗

NASA NTRS2011

Feature Acquisition with Imbalanced Training Data

This work considers cost-sensitive feature acquisition that attempts to classify a candidate datapoint from incomplete information. In this task, an agent acquires features of the datapoint using one or more costly diagnostic tests, and eventually ascribes a classification label. A cost function describes both the penalties for feature acquisition, as well as misclassification errors. A common solution is a Cost Sensitive Decision Tree (CSDT), a branching sequence of tests with features acquired at interior decision points and class assignment at the leaves. CSDT's can incorporate a wide range of diagnostic tests and can reflect arbitrary cost structures. They are particularly useful for online applications due to their low computational overhead. In this innovation, CSDT's are applied to cost-sensitive feature acquisition where the goal is to recognize very rare or unique phenomena in real time. Example applications from this domain include four areas. In stream processing, one seeks unique events in a real time data stream that is too large to store. In fault protection, a system must adapt quickly to react to anticipated errors by triggering repair activities or follow- up diagnostics. With real-time sensor networks, one seeks to classify unique, new events as they occur. With observational sciences, a new generation of instrumentation seeks unique events through online analysis of large observational datasets. This work presents a solution based on transfer learning principles that permits principled CSDT learning while exploiting any prior knowledge of the designer to correct both between-class and withinclass imbalance. Training examples are adaptively reweighted based on a decomposition of the data attributes. The result is a new, nonparametric representation that matches the anticipated attribute distribution for the target events.

Thompson, David R.↗

NASA NTRS2005

Adaptive Filtering Using Recurrent Neural Networks

A method for adaptive (or, optionally, nonadaptive) filtering has been developed for estimating the states of complex process systems (e.g., chemical plants, factories, or manufacturing processes at some level of abstraction) from time series of measurements of system inputs and outputs. The method is based partly on the fundamental principles of the Kalman filter and partly on the use of recurrent neural networks. The standard Kalman filter involves an assumption of linearity of the mathematical model used to describe a process system. The extended Kalman filter accommodates a nonlinear process model but still requires linearization about the state estimate. Both the standard and extended Kalman filters involve the often unrealistic assumption that process and measurement noise are zero-mean, Gaussian, and white. In contrast, the present method does not involve any assumptions of linearity of process models or of the nature of process noise; on the contrary, few (if any) assumptions are made about process models, noise models, or the parameters of such models. In this regard, the method can be characterized as one of nonlinear, nonparametric filtering. The method exploits the unique ability of neural networks to approximate nonlinear functions. In a given case, the process model is limited mainly by limitations of the approximation ability of the neural networks chosen for that case. Moreover, despite the lack of assumptions regarding process noise, the method yields minimum- variance filters. In that they do not require statistical models of noise, the neural- network-based state filters of this method are comparable to conventional nonlinear least-squares estimators.

Parlos, Alexander G.↗

NASA NTRS2011

Binomial Test Method for Determining Probability of Detection Capability for Fracture Critical Applications

The capability of an inspection system is established by applications of various methodologies to determine the probability of detection (POD). One accepted metric of an adequate inspection system is that for a minimum flaw size and all greater flaw sizes, there is 0.90 probability of detection with 95% confidence (90/95 POD). Directed design of experiments for probability of detection (DOEPOD) has been developed to provide an efficient and accurate methodology that yields estimates of POD and confidence bounds for both Hit-Miss or signal amplitude testing, where signal amplitudes are reduced to Hit-Miss by using a signal threshold Directed DOEPOD uses a nonparametric approach for the analysis or inspection data that does require any assumptions about the particular functional form of a POD function. The DOEPOD procedure identifies, for a given sample set whether or not the minimum requirement of 0.90 probability of detection with 95% confidence is demonstrated for a minimum flaw size and for all greater flaw sizes (90/95 POD). The DOEPOD procedures are sequentially executed in order to minimize the number of samples needed to demonstrate that there is a 90/95 POD lower confidence bound at a given flaw size and that the POD is monotonic for flaw sizes exceeding that 90/95 POD flaw size. The conservativeness of the DOEPOD methodology results is discussed. Validated guidelines for binomial estimation of POD for fracture critical inspection are established.

Generazio, Edward R.↗

NASA NTRS2005

Algorithm for Identifying Erroneous Rain-Gauge Readings

An algorithm analyzes rain-gauge data to identify statistical outliers that could be deemed to be erroneous readings. Heretofore, analyses of this type have been performed in burdensome manual procedures that have involved subjective judgements. Sometimes, the analyses have included computational assistance for detecting values falling outside of arbitrary limits. The analyses have been performed without statistically valid knowledge of the spatial and temporal variations of precipitation within rain events. In contrast, the present algorithm makes it possible to automate such an analysis, makes the analysis objective, takes account of the spatial distribution of rain gauges in conjunction with the statistical nature of spatial variations in rainfall readings, and minimizes the use of arbitrary criteria. The algorithm implements an iterative process that involves nonparametric statistics.

Rickman, Doug↗

NASA NTRS2012

A Deterministic Annealing Approach to Clustering AIRS Data

We will examine the validity of means and standard deviations as a basis for climate data products. We will explore the conditions under which these two simple statistics are inadequate summaries of the underlying empirical probability distributions by contrasting them with a nonparametric, method called Deterministic Annealing technique

clustering↗

NASA NTRS2012

Bootstrap Prediction Intervals in Non-Parametric Regression with Applications to Anomaly Detection

Prediction intervals provide a measure of the probable interval in which the outputs of a regression model can be expected to occur. Subsequently, these prediction intervals can be used to determine if the observed output is anomalous or not, conditioned on the input. In this paper, a procedure for determining prediction intervals for outputs of nonparametric regression models using bootstrap methods is proposed. Bootstrap methods allow for a non-parametric approach to computing prediction intervals with no specific assumptions about the sampling distribution of the noise or the data. The asymptotic fidelity of the proposed prediction intervals is theoretically proved. Subsequently, the validity of the bootstrap based prediction intervals is illustrated via simulations. Finally, the bootstrap prediction intervals are applied to the problem of anomaly detection on aviation data.

Kumar, Sricharan↗

NASA NTRS2013

Studies in Astronomical Time Series Analysis. VI. Bayesian Block Representations

This paper addresses the problem of detecting and characterizing local variability in time series and other forms of sequential data. The goal is to identify and characterize statistically significant variations, at the same time suppressing the inevitable corrupting observational errors. We present a simple nonparametric modeling technique and an algorithm implementing it-an improved and generalized version of Bayesian Blocks [Scargle 1998]-that finds the optimal segmentation of the data in the observation interval. The structure of the algorithm allows it to be used in either a real-time trigger mode, or a retrospective mode. Maximum likelihood or marginal posterior functions to measure model fitness are presented for events, binned counts, and measurements at arbitrary times with known error distributions. Problems addressed include those connected with data gaps, variable exposure, extension to piece- wise linear and piecewise exponential representations, multivariate time series data, analysis of variance, data on the circle, other data modes, and dispersed data. Simulations provide evidence that the detection efficiency for weak signals is close to a theoretical asymptotic limit derived by [Arias-Castro, Donoho and Huo 2003]. In the spirit of Reproducible Research [Donoho et al. (2008)] all of the code and data necessary to reproduce all of the figures in this paper are included as auxiliary material.

signal detection↗

NASA NTRS2016

Tropical Cyclone Activity in the North Atlantic Basin During the Weather Satellite Era, 1960-2014

This Technical Publication (TP) represents an extension of previous work concerning the tropical cyclone activity in the North Atlantic basin during the weather satellite era, 1960-2014, in particular, that of an article published in The Journal of the Alabama Academy of Science. With the launch of the TIROS-1 polar-orbiting satellite in April 1960, a new era of global weather observation and monitoring began. Prior to this, the conditions of the North Atlantic basin were determined only from ship reports, island reports, and long-range aircraft reconnaissance. Consequently, storms that formed far from land, away from shipping lanes, and beyond the reach of aircraft possibly could be missed altogether, thereby leading to an underestimate of the true number of tropical cyclones forming in the basin. Additionally, new analysis techniques have come into use which sometimes has led to the inclusion of one or more storms at the end of a nominal hurricane season that otherwise would not have been included. In this TP, examined are the yearly (or seasonal) and 10-year moving average (10-year moving average) values of the (1) first storm day (FSD), last storm day (LSD), and length of season (LOS); (2) frequencies of tropical cyclones (by class); (3) average peak 1-minute sustained wind speed ( ) and average lowest pressure ( ); (4) average genesis location in terms of north latitudinal ( ) and west longitudinal ( ) positions; (5) sum and average power dissipation index ( ); (6) sum and average accumulated cyclone energy ( ); (7) sum and average number of storm days ( ); (8) sum of the number of hurricane days (NHD) and number of major hurricane days (NMHD); (9) net tropical cyclone activity index (NTCA); (10) largest individual storm (LIS) PWS, LP, PDI, ACE, NSD, NHD, NMHD; and (11) number of category 4 and 5 hurricanes (N4/5). Also examined are the December-May (D-M) and June-November (J-N) averages and 10-year moving average values of several climatic factors, including the (1) oceanic Nino index ( ); (2) Atlantic multi-decadal oscillation ( ) index; (3) Atlantic meridional mode ( ) index; (4) global land-ocean temperature index ( ); and (5) quasi-biennial oscillation ( ) index. Lastly, the associational aspects (using both linear and nonparametric statistical tests) between selected tropical cyclone parameters and the climatic factors are examined based on their 10-year moving average trend values.

satellite↗

NASA NTRS2016

Uncertainty Estimates of Psychoacoustic Thresholds Obtained from Group Tests

Adaptive psychoacoustic test methods, in which the next signal level depends on the response to the previous signal, are the most efficient for determining psychoacoustic thresholds of individual subjects. In many tests conducted in the NASA psychoacoustic labs, the goal is to determine thresholds representative of the general population. To do this economically, non-adaptive testing methods are used in which three or four subjects are tested at the same time with predetermined signal levels. This approach requires us to identify techniques for assessing the uncertainty in resulting group-average psychoacoustic thresholds. In this presentation we examine the Delta Method of frequentist statistics, the Generalized Linear Model (GLM), the Nonparametric Bootstrap, a frequentist method, and Markov Chain Monte Carlo Posterior Estimation and a Bayesian approach. Each technique is exercised on a manufactured, theoretical dataset and then on datasets from two psychoacoustics facilities at NASA. The Delta Method is the simplest to implement and accurate for the cases studied. The GLM is found to be the least robust, and the Bootstrap takes the longest to calculate. The Bayesian Posterior Estimate is the most versatile technique examined because it allows the inclusion of prior information.

Rathsam, Jonathan↗

NASA NTRS2016

Confidence Intervals for Laboratory Sonic Boom Annoyance Tests

Commercial supersonic flight is currently forbidden over land because sonic booms have historically caused unacceptable annoyance levels in overflown communities. NASA is providing data and expertise to noise regulators as they consider relaxing the ban for future quiet supersonic aircraft. One deliverable NASA will provide is a predictive model for indoor annoyance to aid in setting an acceptable quiet sonic boom threshold. A laboratory study was conducted to determine how indoor vibrations caused by sonic booms affect annoyance judgments. The test method required finding the point of subjective equality (PSE) between sonic boom signals that cause vibrations and signals not causing vibrations played at various amplitudes. This presentation focuses on a few statistical techniques for estimating the interval around the PSE. The techniques examined are the Delta Method, Parametric and Nonparametric Bootstrapping, and Bayesian Posterior Estimation.

Rathsam, Jonathan↗

NASA NTRS2017

Asymmetric Expansion of the Youngest Galactic Supernova Remnant G1.9+0.3

The youngest Galactic supernova remnant (SNR) G1.9+0.3, produced by a (probable) SN Ia that exploded approximately 1900 CE, is strongly asymmetric at radio wavelengths, much brighter in the north, but bilaterally symmetric in X-rays. We present the results of X-ray expansion measurements that illuminate the origin of the radio asymmetry. We confirm the mean expansion rate (2011-2015) of 0.58% per yr, but large spatial variations are present. Using the nonparametric 'Demons' method, we measure the velocity field throughout the entire SNR, finding that motions vary by a factor of 5, from 0.''09 to 0.''44 per yr. The slowest shocks are at the outer boundary of the bright northern radio rim, with velocities v(sub s) as low as 3600 km per sec (for an assumed distance of 8.5 kpc), much less than v(sub s) = 12,000-13,000 km per sec along the X-ray-bright major axis. Such strong deceleration of the northern blast wave most likely arises from the collision of SN ejecta with a much denser than average ambient medium there. This asymmetric ambient medium naturally explains the radio asymmetry. In several locations, significant morphological changes and strongly nonradial motions are apparent. The spatially integrated X-ray flux continues to increase with time. Based on Chandra observations spanning 8.3 yr, we measure its increase at 1.3% +/- 0.8% per yr. The SN ejecta are likely colliding with the asymmetric circumstellar medium ejected by the SN progenitor prior to its explosion.

ISM: individual objects (G1.9+0.3) ISM: individual↗