A general approach for generating natural random variables
Random numbers generation from natural random phenomena, discussing advantages, circuit and sampling rate
Murry, H. F.↗
SEARCH · Engineering Papers
Results for “random sampling”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Preliminary stratigraphy of the Apollo 15 drill core
The crew of Apollo 15 collected a 242-cm-long core of the regolith developed on the surface of Palus Putredinus (3 deg 39 min 20 sec E, 26 deg 26 min 00 sec N). This core consists of 42 major textural units, which range from a few millimeters to 13 cm thick. The regolith at this site was deposited layer by layer, each layer having been reworked, to some degree, by micrometeorite bombardment. The particle composition of six layers for which samples were obtained varies in a random fashion, thus supporting the heterogeneous, layered model of the regolith. The ratio of lithic fragments derived from the Apennine Front to that derived from the mare surface is about 1:1. This lithic ratio, in a regolith section 3.75 km from the base of the front, is an indication of the efficiency of lateral transport of soil by impact processes and mass wasting.
Heiken, G.↗
The bulk-Moon MgO/FeO ratio: A highlands perspective
Compositional data for nonmare (highlands) samples suggest that the Moon's mg ratio (MgO/FeO) is higher than general estimates. Geochemically representative highlands soils have mg ratios of 0.66 (Apollo 16), 0.69 (Luna 20) and 0.73 (ALHA81005). These soils are mixtures of unrelated pristine nonmare rocks, of which there are at least three groups: Mg-rich rocks, ferroan anorthosites, and KREEP. Other than Mg-rich rocks, virtually all pristine rocks have mg 0.65. Thus, assuming the mixing process that sampled Mg-rich materials was random, the average mg of Mg-rich parent magmas was probably at least 0.70. More direct evidence can be derived from the Mg-rich rocks themselves. Nine of them have bulk-rock mg 0.87. Two (15445 A and 67435 PST) contain Fo(92) olivine. Production of melts that crystallized Fo(92) olivine implies that the mg ratios of source regions in lunar mantle were commensurably high. A quantification of this constraint is developed assuming that the parent melts formed by equilibrium (batch) partial melting. Implications of the model are discussed.
Warren, P. H.↗
Evaluation of a procedure for the analysis of nonstationary vibroacoustic data
Numerical techniques for the spectral analysis of vibration data from space-vehicle launches are described and demonstrated. A nonstationary product model described by Bendat and Piersol (1986) and its locally stationary version (Silverman, 1957) are applied to Space Shuttle flight data, and the results are presented in extensive graphs. It is shown that the nonstationary model can analyze data from longer sampling periods and thus significantly reduce random error; this in turn leads to vibration spectra lower than those obtained with short-duration models.
Himelblau, Harry↗
Double troughs in broad absorption line quasars and Ly-alpha-N V line-locking
It was investigated whether the double trough (DT) structure in the mean C IV BAL trough reported by Weymann et al. (1991) is real or due to statistical fluctuations of BAL troughs over random outflow velocities in a limited sample. A sample of 72 BAL QSOs with C IV BAL troughs was analyzed. It is found that only 22 percent of the sample explicitly exhibits the DT feature; when present the DTs are deep. A Monte Carlo simulation of the mean C IV BAL suggests that the DT feature is real at only the 95-98 percent level.
Korista, Kirk T.↗
Pharmacodynamics of Promethazine in Human Subjects
Promethazine (PMZ) is the drug of choice for the treatment of symptoms associated with space motion sickness in astronauts. Side effects of PMZ include sedation, dizziness and cognitive performance impairment. In this study, we examined pharmacodynamics (PD) in human subjects and validated methods for evaluating cognitive performance effects of medications in space. METHODS: PMZ (12.5,25, and 50 mg) or placebo was administered by IM injection to human subjects in a randomized double-blind treatment design. Samples and data were collected for 72 h post dose. PD evaluation was performed using a battery of performance tests administered using WinSCAT (Windows based Space Cognitive Assessment Test) on a laptop computer, and ARES (ANAM Readiness Evaluation System) on a PDA, plasma concentrations of PMZ were measured using a LC-MS method. RESULTS: Results indicate a linear correlation between PMZ concentration and cognitive performance parameters (p<0.01). Test accuracy decreased and test completion time and response time increased significantly with increasing plasma PMZ concentration. CONCLUSIONS: These results suggest a concentration dependent decrement in cognitive performance associated with PMZ. WinSCAT and ARES are sensitive tools for the assessment PMZ PD and may be applicable for such evaluations with other neurocognitive drugs.
Gatlin, K. T.↗
Artificial Neural Networks to Predict Cognitive Impairment of Rodents Subjected to Space Radiation
INTRODUCTION We use artificial neural networks (ANNs) as an example machine learning (ML) tool to predict the cognitive performance impairment of rats induced by irradiation. The experimental data in the analyses is attentional set-shifting (ATSET) test scores from a rodent model exposed to ≤15 cGy of individual galactic cosmic radiation (GCR) ions: 4He, 28Si, or 56Fe, expected for a Lunar or Mars mission [1]. This work investigates rats at a subject-based level and uses applied dose and performance scores taken before irradiation to predict whether a rat will be impaired when irradiated. The results of this study are significant to crewed space missions as they support the potential of predicting an astronaut’s impairment in a specific task before spaceflight through the implementation of appropriately trained ML tools. METHODS Data used in this work are scores from the ATSET, a multi-stage constrained cognitive flexibility test [2]. Our computational model utilizes the number of attempts to reach the criterion to pass a stage as a behavioral performance measure for rats. We use the post-irradiation scores, generate thresholds from cumulative distribution plots of non-irradiated rats, and calculate the percent of irradiated rats whose scores fall below the threshold to infer how each radiation type/dose affects a population. Rats scoring above the threshold are labeled impaired while the others are non-impaired. We then employ ANNs as a typical ML technique, and use each subject’s individual scores taken before radiation along with the applied dose, to predict their personal susceptibility to cognitive impairment due to space radiation exposure. RESULTS AND CONCLUSION A significant finding is the exhibition of a dose-dependent increasing probability of impairment for 1 to 10 cGy of 28Si or 56Fe in the simple discrimination (SD) stage of the ATSET, and for 1 to 10 cGy of 56Fe in the compound discrimination (CD) stage. On a subject-based level, implementing ML classifiers such as ANNs identifies rats that have a higher tendency for impairment after GCR exposure [1]. The receiver operating characteristic (ROC) and the precision-recall (PR) curves of the ML models show a better prediction of impairment when 56Fe is the ion in question in both SD (Figure 1) and CD stages. They, however, do not depict impairment due to 4He in SD (Figure 1) and 28Si in CD, suggesting no dose-dependent impairment response in these cases. In this work, “good” prediction pertains to “better-than-random-chance”, due to the limited sample size and the high inter- and intra-individual variabilities in response to brain stimulation paradigms, as applicable to both animals and humans. More behavioral tests and biomarkers should be investigated on the same subjects, to be fed to the ML models to capture the agents responsible for performance alterations of some individuals versus others.
machine learning↗
Machine Learning Models to Predict Cognitive Impairment of Rodents Subjected to Space Radiation
INTRODUCTION We use artificial neural networks (ANNs) as an example machine learning (ML) tool to predict the cognitive performance impairment of rats induced by irradiation. The experimental data in the analyses is attentional set-shifting (ATSET) test scores from a rodent model exposed to ≤15 cGy of individual galactic cosmic radiation (GCR) ions: 4He, 28Si, or 56Fe, expected for a Lunar or Mars mission [1]. This work investigates rats at a subject-based level and uses applied dose and performance scores taken before irradiation to predict whether a rat will be impaired when irradiated. The results of this study are significant to crewed space missions as they support the potential of predicting an astronaut’s impairment in a specific task before spaceflight through the implementation of appropriately trained ML tools. METHODS Data used in this work are scores from the ATSET, a multi-stage constrained cognitive flexibility test [2]. Our computational model utilizes the number of attempts to reach the criterion to pass a stage as a behavioral performance measure for rats. We use the post-irradiation scores, generate thresholds from cumulative distribution plots of non-irradiated rats, and calculate the percent of irradiated rats whose scores fall below the threshold to infer how each radiation type/dose affects a population. Rats scoring above the threshold are labeled impaired while the others are non-impaired. We then employ ANNs as a typical ML technique, and use each subject’s individual scores taken before radiation along with the applied dose, to predict their personal susceptibility to cognitive impairment due to space radiation exposure. RESULTS AND CONCLUSION A significant finding is the exhibition of a dose-dependent increasing probability of impairment for 1 to 10 cGy of 28Si or 56Fe in the simple discrimination (SD) stage of the ATSET, and for 1 to 10 cGy of 56Fe in the compound discrimination (CD) stage. On a subject-based level, implementing ML classifiers such as ANNs identifies rats that have a higher tendency for impairment after GCR exposure [1]. The receiver operating characteristic (ROC) and the precision-recall (PR) curves of the ML models show a better prediction of impairment when 56Fe is the ion in question in both SD (Figure 1) and CD stages. They, however, do not depict impairment due to 4He in SD (Figure 1) and 28Si in CD, suggesting no dose-dependent impairment response in these cases. In this work, “good” prediction pertains to “better-than-random-chance”, due to the limited sample size and the high inter- and intra-individual variabilities in response to brain stimulation paradigms, as applicable to both animals and humans. More behavioral tests and biomarkers should be investigated on the same subjects, to be fed to the ML models to capture the agents responsible for performance alterations of some individuals versus others.
machine learning↗
Front Range Wildland Fires: Evaluating the Efficacy of Remote Sensing Imagery in Monitoring Forest Fuels Treatment Methods
Over the last several decades, wildfire frequency and severity in forested areas along Colorado’s Front Range have increased due to a buildup of fuels. This has led to an increase in forest treatments, as well as an increased need to evaluate the success of these treatments. Remote sensing products offer an efficient and cost-effective way to monitor forest treatments; however, not all remote sensing products and analysis techniques have been explored by Coloradan land managers. Specifically, project partners at the Colorado State Forest Service (CSFS) and the Colorado Forest Restoration Institute (CFRI) were interested in using an effective and streamlined method of mapping canopy cover to better monitor forest treatment success. To support their needs, the NASA DEVELOP Front Range Wildland Fires team explored National Agricultural Imagery Program (NAIP) imagery at different spatial resolutions and numbers of training points with NASA’s Shuttle Radar Topography Mission (SRTM) Data Elevation Model (DEM) as a predictor in addition to NAIP imagery spectral predictors. From this analysis, we created classified canopy cover rasters, and compared accuracy metrics across model iterations. We also determined that the best performing model, with an overall accuracy of 0.900 uses 2021 NAIP imagery at 2-meter resolution, 800 training points, 200 testing points, does not use topographic predictors, and reclassifies shadow pixels via a pre-selected NDVI threshold.
Remote Sensing↗
The Petrology and Geochemistry of Feldspathic Granulitic Breccia NWA 3163: Implications for the Lunar Crust
Lunar meteorites are crucial to understand the Moon s geological history because, being samples of the lunar crust that have been ejected by random impact events, they potentially originate from areas outside the small regions of the lunar surface sampled by the Apollo and Luna missions. The Apollo and Luna sample sites are contained within the Procellarum KREEP Terrain (PKT, Jolliff et al., 2000), where KREEP refers to potassium, rare earth element, and phosphorus-rich lithologies. The KREEP-rich rocks in the PKT are thought to be derived from late-stage residual liquids after approx.95-99% crystallization of a lunar magma ocean (LMO). These are understood to represent late-stage liquids which were enriched in incompatible trace elements (ITE) relative to older rocks (Snyder et al., 1992). As a consequence, the PKT is a significant reservoir for Th and KREEP. However, the majority of the lunar surface is likely to be significantly more depleted in ITE (84%, Jolliff et al., 2000). Lunar meteorites that are low in KREEP and Th may thus sample regions distinct from the PKT and are therefore a valuable source of information regarding the composition of KREEP-poor lunar crust. Northwest Africa (NWA) 3163 is a thermally metamorphosed ferroan, feldspathic, granulitic breccia composed of igneous clasts with a bulk anorthositic, noritic bulk composition. It is relatively mafic (approx.5.8 wt.% FeO; approx.5 wt.% MgO) and has some of the lowest concentrations of ITEs (17ppm Ba) compared to the feldspathic lunar meteorite (FLM) and Apollo sample suites (Hudgins et al., 2011). Localized plagioclase melting and incipient melting of mafic minerals require localized peak shock pressures in excess of 45 GPa (Chen and El Goresy, 2000; Hiesinger and Head, 2006). NWA 3163, and paired samples NWA 4481 and 4883, have previously been interpreted to represent an annealed micro-breccia which was produced by burial metamorphism at depth in the ancient lunar crust (Fernandes et al., 2009). This is in contrast to the interpretation of Hudgins et al. (2009) where NWA 3163 was interpreted to have formed through contact metamorphism. To further constrain its origin, we examine the petrogenesis of NWA 3163 with a particular emphasis on in-situ measurement of trace elements within constituent minerals, Sm-Nd and Rb-Sr isotopic systematics on separated mineral fractions and petrogenetic modeling.
McLeod, C. L.↗
Integrated Communication and Control Systems. III - Nonidentical sensor and controller sampling
Networking in Integrated Communication and Control Systems (ICCS) introduces randomly varying delays which degrade the system dynamic performance and are a source of potential instability. In Part I of this sequence of papers, a discrete-time, finite-dimensional model of the delayed control system for analysis and design of ICCS was developed where the sensor and controller have identical sampling rates. In Part II, two alternate approaches for ICCS design, namely, identical and nonidentical sampling rates for sensor and controller were proposed. This Part III presents extended modeling of ICCS for nonidentical sensor and controller sampling rates. This model is also suitable for analyzing tracking problems, i.e., control systems with time-dependent reference inputs.
Liou, Luen-Woei↗
Materials for Heated Head Automated Thermoplastic Tape Placement
NASA Langley Research Center (LaRC) is currently pursuing multiple paths to develop out of autoclave (OOA) polymeric composite materials and processes. Polymeric composite materials development includes the synthesis of new and/or modified thermosetting and thermoplastic matrix resins designed for specific OOA processes. OOA processes currently under investigation include vacuum bag only (VBO) prepreg/composite fabrication, resin transfer molding (RTM), vacuum assisted resin transfer molding (VARTM) and heated head automated thermoplastic tape placement (HHATP). This paper will discuss the NASA Langley HHATP facility and capabilities and recent work on characterizing thermoplastic tape quality and requirements for quality part production. Samples of three distinct versions of APC-2 (AS4/PEEK) thermoplastic dry tape were obtained from two materials vendors, TENCATE, Inc. and CYTEC Engineered Materials** (standard grade and an experimental batch). Random specimens were taken from each of these samples and subjected to photo-microscopy and surface profilometry. The CYTEC standard grade of APC-2 tape had the most voids and splits and the highest surface roughness and/or waviness. Since the APC-2 tape is composed of a thermoplastic matrix, it offers the flexibility of reprocessing to improve quality, and thereby improve final quality of HHATP laminates. Discussions will also include potential research areas and future work that is required to advance the state of the art in the HHATP process for composite fabrication.
Jensen, Brian J.↗
A count probability cookbok: Spurious effects and the scaling model
We study the errors brought by finite volume effects and dilution effects on the practical determination of the count probability distribution function P(sub N)(n,l), which is the probability of having N objects in a cell of volume l cubed for a set of average number density n. Dilution effects are particularly revelant to the so-called sparse sampling strategy. This work is mainly done in the framework of the Bailan & Schaeffer scaling model, which assumes that the Q-body correlation functions obey the scaling relation Xi(sub Q)(lambda r(sub l),....lambda r(sub Q) = lambda(exp -(Q-1)gamma) Xi(sub Q)(r(sub 1),....r(sub Q)). We use three synthetic samples as references to perform our analysis: a fractal generated by a Rayleigh-Levy random walk with approximately 3 x 10(exp 4) objects, a sample dominated by a spherical power-law cluster with approximately 3 x 10(exp 4) objects and a cold dark matter (CDM) universe involving approximately 3 x 10(exp 5) matter particles.
Colombi, S.↗
The topology of large-scale structure. I - Topology and the random phase hypothesis
Many models for the formation of galaxies and large-scale structure assume a spectrum of random phase (Gaussian), small-amplitude density fluctuations as initial conditions. In such scenarios, the topology of the galaxy distribution on large scales relates directly to the topology of the initial density fluctuations. Here a quantitative measure of topology - the genus of contours in a smoothed density distribution - is described and applied to numerical simulations of galaxy clustering, to a variety of three-dimensional toy models, and to a volume-limited sample of the CfA redshift survey. For random phase distributions the genus of density contours exhibits a universal dependence on threshold density. The clustering simulations show that a smoothing length of 2-3 times the mass correlation length is sufficient to recover the topology of the initial fluctuations from the evolved galaxy distribution. Cold dark matter and white noise models retain a random phase topology at shorter smoothing lengths, but massive neutrino models develop a cellular topology.
Weinberg, David H.↗
The ergodic decomposition of stationary discrete random processes
The ergodic decomposition is discussed, and a version focusing on the structure of individual sample functions of stationary processes is proved for the special case of discrete-time random processes with discrete alphabets. The result is stronger in this case than the usual theorem, and the proof is both intuitive and simple. Estimation-theoretic and information-theoretic interpretations are developed and applied to prove existence theorems for universal source codes, both noiseless and with a fidelity criterion.
Gray, R. M.↗
A Markov Random Field Model for Texture-based Segmentation of Small Cracks in Thin InconelTubes
Markov random fields have been used for image segmentation since their introduction in the1980s. This work applies a method from Principal Component Thermography to enhance thecontrast in damage regions in 3D images derived from X-ray computed tomography (CT)inspections. The developed method is applied to sizing of small cracks in thin Inconel tubes designed as probability of detection (POD) samples for radiographic inspection.Misclassification errors arising from artifacts due to beam-hardening are reduced by fitting the boundary of the segmented damage region with the arc of an ellipse. Results are comparedagainst those obtained through manual inspection.
Image segmentation↗
A Practical Methodology for Quantifying Random and Systematic Components of Unexplained Variance in a Wind Tunnel
This paper documents a check standard wind tunnel test conducted in the Langley 0.3-Meter Transonic Cryogenic Tunnel (0.3M TCT) that was designed and analyzed using the Modern Design of Experiments (MDOE). The test designed to partition the unexplained variance of typical wind tunnel data samples into two constituent components, one attributable to ordinary random error, and one attributable to systematic error induced by covariate effects. Covariate effects in wind tunnel testing are discussed, with examples. The impact of systematic (non-random) unexplained variance on the statistical independence of sequential measurements is reviewed. The corresponding correlation among experimental errors is discussed, as is the impact of such correlation on experimental results generally. The specific experiment documented herein was organized as a formal test for the presence of unexplained variance in representative samples of wind tunnel data, in order to quantify the frequency with which such systematic error was detected, and its magnitude relative to ordinary random error. Levels of systematic and random error reported here are representative of those quantified in other facilities, as cited in the references.
Deloach, Richard↗
Combined Uncertainty and A-Posteriori Error Bound Estimates for General CFD Calculations: Theory and Software Implementation
This workshop presentation discusses the design and implementation of numerical methods for the quantification of statistical uncertainty, including a-posteriori error bounds, for output quantities computed using CFD methods. Hydrodynamic realizations often contain numerical error arising from finite-dimensional approximation (e.g. numerical methods using grids, basis functions, particles) and statistical uncertainty arising from incomplete information and/or statistical characterization of model parameters and random fields. The first task at hand is to derive formal error bounds for statistics given realizations containing finite-dimensional numerical error [1]. The error in computed output statistics contains contributions from both realization error and the error resulting from the calculation of statistics integrals using a numerical method. A second task is to devise computable a-posteriori error bounds by numerically approximating all terms arising in the error bound estimates. For the same reason that CFD calculations including error bounds but omitting uncertainty modeling are only of limited value, CFD calculations including uncertainty modeling but omitting error bounds are only of limited value. To gain maximum value from CFD calculations, a general software package for uncertainty quantification with quantified error bounds has been developed at NASA. The package provides implementations for a suite of numerical methods used in uncertainty quantification: Dense tensorization basis methods [3] and a subscale recovery variant [1] for non-smooth data, Sparse tensorization methods[2] utilizing node-nested hierarchies, Sampling methods[4] for high-dimensional random variable spaces.
CFD↗