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Results for “Likelihood Function”

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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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS1993

Luminosity functions for very low mass stars and brown dwarfs

A theoretical investigation of the luminosity function for low-mass objects to constrain the stellar initial mass function at the low-mass end is reported. The ways in which luminosity functions for low-mass stars are affected by star formation histories, brown dwarf and premain-sequence cooling rates and main-sequence mass luminosity relations, and the IMF are examined. Cooling rates and the mass-luminosity relation are determined through a new series of evolutionary calculations for very low mass stars and brown dwarfs in the range 0.05-0.50 solar mass. Model luminosity functions are constructed for specific comparison with the results of four recent observational surveys. The likelihood that the stellar mass function in the solar neighborhood is increasing at masses near the bottom of the main sequence and perhaps at lower masses is confirmed. In the most optimistic case, brown dwarfs contribute half of the local missing disk mass. The actual contribution is likely to be considerably less.

Laughlin, Gregory↗
NASA NTRS1980

The sensitivity of a very long baseline interferometer

The theoretical sensitivity of various methods of acquiring and processing interferometer data are compared. It is shown that for a fixed digital recording capacity one bit quantization of single sideband data filtered with a rectangular bandpass and sampled at the Nyquist rate yields the optimum signal to noise ratio. The losses which result from imperfect bandpass, poor image rejection, approximate methods of fringe rotation, fractional bit correction, and loss of quadrature are discussed. Also discussed is the use of the complex delay function as a maximum likelihood fringe estimator.

Rogers, A. E. E.↗
NASA NTRS2001

Evidence-Based Approach to the Analysis of Serious Decompression Sickness with Application to EVA Astronauts

It is important to understand the risk of serious hypobaric decompression sickness (DCS) in order to develop procedures and treatment responses to mitigate the risk. Since it is not ethical to conduct prospective tests about serious DCS with humans, the necessary information was gathered from 73 published reports. We hypothesize that a 4-hr 100% oxygen (O2) prebreathe results in a very low risk of serious DCS, and test this through analysis. We evaluated 258 tests containing information from 79,366 exposures in attitude chambers. Serious DCS was documented in 918 men during the tests. Serious DCS are signs and symptoms broadly classified as Type II DCS. A risk function analysis with maximum likelihood optimization was performed to identify significant explanatory variables, and to create a predictive model for the probability of serious DCS [P(serious DCS)]. Useful variables were Tissue Ratio, the planned time spent at altitude (T(sub alt)), and whether or not repetitive exercise was performed at altitude. Tissue Ratio is P1N2/P2, where P1N2 is calculated nitrogen (N2) pressure in a compartment with a 180-min half-time for N2 pressure just before ascent, and P2 is ambient pressure after ascent. A prebreathe and decompression profile Shuttle astronauts use for extravehicular activity (EVA) includes a 4-hr prebreathe with 100% O2, an ascent to P2 = 4.3 lb per sq. in. absolute, and a T(sub alt) = 6 hr. The P(serious DCS) is: 0.0014 (0.00096 - 0.00196, 95% confidence interval) with exercise and 0.00025 (0.00016 - 0.00035) without exercise. Given 100 Shuttle EVAs to date and no report of serious DCS, the true risk is less than 0.03 with 95% confidence (Binomial Theorem). It is problematic to estimate the risk of serious DCS since it appears infrequently, even if the estimate is based on thousands of altitude chamber exposures. The true risk to astronauts may lie between the extremes of the confidence intervals (0.00016 - 0.00196) since the contribution of other factors, particularly exercise, to the risk of serious DCS during EVA is unknown. A simple model that only accounts for four important variables in retrospective data is still helpful to increase our understanding about the risk of serious DCS.

Conkin, Johnny↗
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

Airport Traffic Conflict Detection and Resolution Algorithm Evaluation

Two conflict detection and resolution (CD&R) algorithms for the terminal maneuvering area (TMA) were evaluated in a fast-time batch simulation study at the National Aeronautics and Space Administration (NASA) Langley Research Center. One CD&R algorithm, developed at NASA, was designed to enhance surface situation awareness and provide cockpit alerts of potential conflicts during runway, taxi, and low altitude air-to-air operations. The second algorithm, Enhanced Traffic Situation Awareness on the Airport Surface with Indications and Alerts (SURF IA), was designed to increase flight crew awareness of the runway environment and facilitate an appropriate and timely response to potential conflict situations. The purpose of the study was to evaluate the performance of the aircraft-based CD&R algorithms during various runway, taxiway, and low altitude scenarios, multiple levels of CD&R system equipage, and various levels of horizontal position accuracy. Algorithm performance was assessed through various metrics including the collision rate, nuisance and missed alert rate, and alert toggling rate. The data suggests that, in general, alert toggling, nuisance and missed alerts, and unnecessary maneuvering occurred more frequently as the position accuracy was reduced. Collision avoidance was more effective when all of the aircraft were equipped with CD&R and maneuvered to avoid a collision after an alert was issued. In order to reduce the number of unwanted (nuisance) alerts when taxiing across a runway, a buffer is needed between the hold line and the alerting zone so alerts are not generated when an aircraft is behind the hold line. All of the results support RTCA horizontal position accuracy requirements for performing a CD&R function to reduce the likelihood and severity of runway incursions and collisions.

Jones, Denise R.↗
NASA NTRS2011

Unsupervised Detection of Planetary Craters by a Marked Point Process

With the launch of several planetary missions in the last decade, a large amount of planetary images is being acquired. Preferably, automatic and robust processing techniques need to be used for data analysis because of the huge amount of the acquired data. Here, the aim is to achieve a robust and general methodology for crater detection. A novel technique based on a marked point process is proposed. First, the contours in the image are extracted. The object boundaries are modeled as a configuration of an unknown number of random ellipses, i.e., the contour image is considered as a realization of a marked point process. Then, an energy function is defined, containing both an a priori energy and a likelihood term. The global minimum of this function is estimated by using reversible jump Monte-Carlo Markov chain dynamics and a simulated annealing scheme. The main idea behind marked point processes is to model objects within a stochastic framework: Marked point processes represent a very promising current approach in the stochastic image modeling and provide a powerful and methodologically rigorous framework to efficiently map and detect objects and structures in an image with an excellent robustness to noise. The proposed method for crater detection has several feasible applications. One such application area is image registration by matching the extracted features.

Troglio, G.↗
NASA NTRSDate not supplied

An Interactive MATLAB Program for Fitting Transfer Functions to Frequency Responses

A computer program called FRFit (Frequency Response Fitting) for matching single-input single-output (SISO) transfer function models to empirical frequency response data is described. The program was written in MATLAB and has a graphical user interface (GUI). It is interactive in that the user manually builds the transfer function model using ``elementary factors'' (gain, delay, differentiators and integrators, and first- and second-order poles and zeros) and adjusts their values with sliders or entry fields. A nonlinear optimization can also be used to determine maximum-likelihood estimates of the transfer function parameters and their associated uncertainties. The program has some usefulness as a teaching aid, and can be applied to model structure determination, reduced-order modeling, preliminary analysis, and other system identification problems. FRFit is demonstrated using example problems, including the identification of aircraft transfer functions and rational function approximations of Theodorsen's function.

Frequency response↗
NASA NTRS1998

Low level CO2 effects on pulmonary function in humans

The purpose of the study was to determine whether chamber exposure to low levels of CO2 results in functional alterations in gas mixing and closing volume in humans. Four healthy volunteer subjects were exposed to 0.7% CO2 and to 1.2% CO2. Spirometry, lung volumes, single breath nitrogen washout, diffusing capacity for carbon monoxide (DLCO) by two methods, and cardiac output were measured in triplicate. Values were obtained over two non-consecutive days during the training period (control) and on days 2 or 3, 4, 6, 10, 13, and 23 of exposure to each CO2 level. Measurements were made during the same time of day. There was one day of testing after exposure, while still in the chamber but off carbon dioxide. The order of testing, up until measurements of DLCO and cardiac output, were randomized to avoid presentation effects. The consistent findings were a reduction in diffusing capacity for carbon monoxide and a fall in cardiac output, occurring to a similar degree with both exposures. For the group as a whole, there was no indication of major effects on spirometry, lung volumes, gas mixing or dead space. We conclude that small changes may occur in the function of distal gas exchanging units; however, these effects were not associated with any adverse health effects. The likelihood of pathophysiologic changes in lung function or structure with 0.7 or 1.2% CO2 exposure for this period of time, is therefore, low.

NASA Discipline Cardiopulmonary↗
NASA NTRSDate not supplied

Sampling Functions from Gaussian Processes and Structured Covariance Gaussian Networks

When learning aerodynamic models from data, it is critical to incorporate estimates of model uncertainty. This motivates the design of probabilistic aerodynamic databases which can be sampled to generate physically and statistically plausible aerodynamic models. In this talk we discuss how to sample deterministic functions from two different kinds of probabilistic models and demonstrate their use. First, Gaussian Process Regressors (GPRs) are a widely used probabilistic kernel-based model which can be thought of as Gaussian distributions over functions. GPRs are generally trained by maximizing the marginal likelihood of seeing the training data over the kernel parameter space. Sample functions are easily generated by drawing points from the Gaussian distribution at desired input points. However, when the points are not known ahead of time, the classical sampling approach is not possible since successive function samples will generate different function realizations. We present an approach for sampling consistent function evaluations from a GPR over multiple samples. Second, we describe a neural network architecture which learns a conditional Gaussian distribution by maximizing the marginal likelihood at each point in the input space. We then discuss and compare several options for generating sample functions which match this distribution. Finally, we demonstrate the use of these probabilistic aerodynamic models in an atmospheric reentry simulation.

Gaussian process regression↗
NASA NTRS1968

Concept for simplified serial digital decoder

Modular decoder, which lends itself best to special purpose digital equipment using sequential access memories, decodes the first order Reed-Muller codes. It functions as a maximum-likelihood exhaustive-search decoder and is a modular implementation to accommodate codes of any length.

Green, R. R.↗
NASA NTRS1976

Nonparametric probability density estimation by optimization theoretic techniques

Two nonparametric probability density estimators are considered. The first is the kernel estimator. The problem of choosing the kernel scaling factor based solely on a random sample is addressed. An interactive mode is discussed and an algorithm proposed to choose the scaling factor automatically. The second nonparametric probability estimate uses penalty function techniques with the maximum likelihood criterion. A discrete maximum penalized likelihood estimator is proposed and is shown to be consistent in the mean square error. A numerical implementation technique for the discrete solution is discussed and examples displayed. An extensive simulation study compares the integrated mean square error of the discrete and kernel estimators. The robustness of the discrete estimator is demonstrated graphically.

Scott, D. W.↗
NASA NTRS1981

A complete X-ray sample of the high latitude sky from HEAO-1 A-2: log N lo S and luminosity functions

An experiment was performed in which a complete X-ray survey of the 8.2 steradians of the sky at galactic latitudes where the absolute value of b is 20 deg down to a limiting sensitivity of 3.1 x ten to the minus 11th power ergs/sq cm sec in the 2-10 keV band. Of the 85 detected sources 17 were identified with galactic objects, 61 were identified with extragalactic objects, and 7 remain unidentified. The log N - log S relation for the non-galactic objects is well fit by the Euclidean relationship. The X-ray spectra of these objects were used to construct log N - log S in physical units. The complete sample of identified sources was used to construct X-ray luminosity functions, using the absolute maximum likelihood method, for clusters galaxies and active galactic nuclei.

Piccinotti, G.↗
NASA NTRS1985

Einstein X-ray survey of the Pleiades - The dependence of X-ray emission on stellar age

The data obtained with two pointed observations of 1 deg by 1 deg fields of the Pleiades region have been analyzed, and the results are presented. The maximum-likelihood X-ray luminosity functions for the Pleiades G and K stars in the cluster are derived, and it is shown that, for the G stars, the Pleiades X-ray luminosity function is significantly brighter than the corresponding function for Hyades G dwarf stars. This finding indicates a dependence of X-ray luminosity on stellar age, which is confirmed by comparison of the same data with median X-ray luminosities of pre-main sequence and local disk population dwarf G stars. It is suggested that the significantly larger number of bright X-ray sources associated with G stars than with K stars, the lack of detection of M stars, and the relatively rapid rotation of the Pleiades K stars can be explained in terms of the onset of internal differential rotation near the convective envelope-radidative core interface after the spin-up phase during evolution to the main sequence.

Micela, G.↗
NASA NTRS1992

X-ray emission at low-mass end of the MS - Results from an extensive Einstein Observatory survey

The 0.16-4.0 keV X-ray emission from K and M stars of luminosity classes IV, V, and VI within 25 parsec of the sun was measured using all available Einstein Observatory IPC data and a critical compilation of catalogued optical data. Fifty four of 88 stars were detected, 70 of 138 M stars with Mv less than 13.4 (corresponding to M6) and 15 or 31 fainter M stars. The surveyed stars were grouped, on the basis of U, V, W space velocity components, into old-disk, young-disk, and halo population stars. Then, a subsample was selected which is statistically representative of the population of K and M stars in the solar neighborhood, on the basis of which unbiased Maximum Likelihood X-ray luminosity functions were constructed for K, early M, and late M stars. The investigation revealed a decrease of X-ray luminosity with increasing stellar age in the range of ages of disk population stars.

Barbera, M.↗
NASA NTRS1993

X-ray emission at the low-mass end - Results from an extensive Einstein Observatory survey

We have used available IPC data and a critical compilation of cataloged optical data to measure the 0.16-3.5 keV X-ray emission from 88 K and 169 M stars of luminosity classes IV, V, and VI within 25 pc from the Sun. The IPC detected 54 out of the 88 K stars, 70 out of the 138 M stars with M(v) less than 13.4, and 15 out of the 31 fainter M stars. We have identified a subsample of surveyed stars that is statistically representative of the population of K and M stars in the solar neighborhood. On the basis of this subsample (1) we have shown the occurrence of a drop in the level of X-ray emission for M stars later than approximately M5; (2) we have built unbiased maximum likelihood X-ray luminosity functions for the K, early M, and late M stars; (3) we have confirmed, both for K and M stars, the decrease of X-ray luminosity with increasing stellar age in the range of ages of disk population stars: and (4) we have shown that no obvious correlation is present between X-ray and bolometric luminosities in the entire representative samples of K and M stars, but only within flare stars which also seem to mark a saturation in X-ray luminosity level.

Barbera, M.↗
NASA NTRS1967

Computer program reduces calculation time of normal response functions

FORTRAN 2 computer program rapidly calculates parameters of maximum likelihood estimates from sensitivity experiment data populations. The program uses the Newton-Raphson iterative procedure to calculate the mean and standard deviation of portions of the cumulative normal response function.

Alexander, M. J.↗
NASA NTRSDate not supplied

Problem Complexity and LLM: H-M Team Reliability in Challenging Environments

In traditional human-machine operations, the functional decomposition of actions and responsibilities among various agents is assigned a priori. For instance, in current air traffic operations, although assisted by software, human pilots have the ultimate control of aircraft. Multiagent human-machine and machine-machine systems will face problems of varying and potentially unpredicted complexity in future challenging scenarios of planetary, en route, and orbital activities. Hence, it is important to enable dynamic transfer of decision-making to appropriate team members, human or machine, depending on which agent is best equipped to solve that specific problem on a time budget. In this paper, we consider aspects of problem-solving and its modeling that affect the outcomes of decision-making as a function of solution quality and the likelihood of solving the problem on a required time budget. We focus on Large Language Models (LLM) as potential machine teammates and conclude that practical, predictive modeling of their performance, at the current stage of their development, is infeasible. Simple examples help us illustrate that current LLM will require fundamental advancements to provide reliable support in team decision-making, especially in safety-critical and time-critical domains. The study is not meant to diminish the value of the remarkable capabilities of LLM, but rather to gain a better understanding of the technology’s appropriate use and the needed additions.

function allocation↗