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NASA NTRS1996

The Non-Axisymmetric Milky Way

The Dwek et al. model represents the current state-of-the-art model for the stellar structure of our Galaxy. The improvements we have made to this model take a number of forms: (1) the construction of a more detailed dust model so that we can extend our modeling to the galactic plane; (2) simultaneous fits to the bulge and the disk; (3) the construction of the first self-consistent model for a galactic bar; and (4) the development and application of algorithms for constructing nonparametric bar models. The improved Galaxy model has enabled a number of exciting science projects. In Zhao et al., we show that the number and duration of microlensing events seen by the OGLE and MACHO collaborations towards the bulge were consistent with the predictions of our bar model. In Malhotra et al., we constructed an infrared Tully-Fisher (TF) relation for the local group. We found the tightest TF relation ever seen in any band and in any group of galaxies. The tightness of the correlation places strong constraints on galaxy formation models and provides a independent check of the Cepheid distance scale.

Spergel, David N.↗

NASA NTRS1996

Comparison of System Identification Techniques for the Hydraulic Manipulator Test Bed (HMTB)

In this thesis linear, dynamic, multivariable state-space models for three joints of the ground-based Hydraulic Manipulator Test Bed (HMTB) are identified. HMTB, housed at the NASA Langley Research Center, is a ground-based version of the Dexterous Orbital Servicing System (DOSS), a representative space station manipulator. The dynamic models of the HMTB manipulator will first be estimated by applying nonparametric identification methods to determine each joint's response characteristics using various input excitations. These excitations include sum of sinusoids, pseudorandom binary sequences (PRBS), bipolar ramping pulses, and chirp input signals. Next, two different parametric system identification techniques will be applied to identify the best dynamical description of the joints. The manipulator is localized about a representative space station orbital replacement unit (ORU) task allowing the use of linear system identification methods. Comparisons, observations, and results of both parametric system identification techniques are discussed. The thesis concludes by proposing a model reference control system to aid in astronaut ground tests. This approach would allow the identified models to mimic on-orbit dynamic characteristics of the actual flight manipulator thus providing astronauts with realistic on-orbit responses to perform space station tasks in a ground-based environment.

Morris, A. Terry↗

NASA NTRS1997

Shape Optimization by Bayesian-Validated Computer-Simulation Surrogates

A nonparametric-validated, surrogate approach to optimization has been applied to the computational optimization of eddy-promoter heat exchangers and to the experimental optimization of a multielement airfoil. In addition to the baseline surrogate framework, a surrogate-Pareto framework has been applied to the two-criteria, eddy-promoter design problem. The Pareto analysis improves the predictability of the surrogate results, preserves generality, and provides a means to rapidly determine design trade-offs. Significant contributions have been made in the geometric description used for the eddy-promoter inclusions as well as to the surrogate framework itself. A level-set based, geometric description has been developed to define the shape of the eddy-promoter inclusions. The level-set technique allows for topology changes (from single-body,eddy-promoter configurations to two-body configurations) without requiring any additional logic. The continuity of the output responses for input variations that cross the boundary between topologies has been demonstrated. Input-output continuity is required for the straightforward application of surrogate techniques in which simplified, interpolative models are fitted through a construction set of data. The surrogate framework developed previously has been extended in a number of ways. First, the formulation for a general, two-output, two-performance metric problem is presented. Surrogates are constructed and validated for the outputs. The performance metrics can be functions of both outputs, as well as explicitly of the inputs, and serve to characterize the design preferences. By segregating the outputs and the performance metrics, an additional level of flexibility is provided to the designer. The validated outputs can be used in future design studies and the error estimates provided by the output validation step still apply, and require no additional appeals to the expensive analysis. Second, a candidate-based a posteriori error analysis capability has been developed which provides probabilistic error estimates on the true performance for a design randomly selected near the surrogate-predicted optimal design.

Patera, Anthony T.↗

NASA NTRS2000

A Human Factors Evaluation of a Methodology for Pressurized Crew Module Acceptability for Zero-Gravity Ingress of Spacecraft

This project aimed to develop a methodology for evaluating performance and acceptability characteristics of the pressurized crew module volume suitability for zero-gravity (g) ingress of a spacecraft and to evaluate the operational acceptability of the NASA crew return vehicle (CRV) for zero-g ingress of astronaut crew, volume for crew tasks, and general crew module and seat layout. No standard or methodology has been established for evaluating volume acceptability in human spaceflight vehicles. Volume affects astronauts'ability to ingress and egress the vehicle, and to maneuver in and perform critical operational tasks inside the vehicle. Much research has been conducted on aircraft ingress, egress, and rescue in order to establish military and civil aircraft standards. However, due to the extremely limited number of human-rated spacecraft, this topic has been un-addressed. The NASA CRV was used for this study. The prototype vehicle can return a 7-member crew from the International Space Station in an emergency. The vehicle's internal arrangement must be designed to facilitate rapid zero-g ingress, zero-g maneuverability, ease of one-g egress and rescue, and ease of operational tasks in multiple acceleration environments. A full-scale crew module mockup was built and outfitted with representative adjustable seats, crew equipment, and a volumetrically equivalent hatch. Human factors testing was conducted in three acceleration environments using ground-based facilities and the KC-135 aircraft. Performance and acceptability measurements were collected. Data analysis was conducted using analysis of variance and nonparametric techniques.

Sanchez, Merri J.↗

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↗