Development of a Traumatic Abdominal Injury Module for Estimation of Injury Probability During an International Space Station Mission
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Lewandowski, Beth↗
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Results for “Probability estimates”
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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Orbit Determination and Estimation of Impact Probability for Near Earth Objects
Near-Earth Objects (NEOs) are asteroids and comets that have been nudged by the gravitational attraction of nearby planets into orbits that allow them to enter the Earth's neighborhood.
Asteroids↗
Maximum likelihood estimation of label imperfection probabilities and its use in the identification of mislabeled patterns
Estimating label imperfections and the use of estimations in the identification of mislabeled patterns are discussed. Expressions are presented for the asymptotic variances of the probability of correct classification and proportion, and for the maximum likelihood estimates of classification errors and a priori probabilities. Models are developed for imperfections in the labels and classification errors, and expressions are derived for the probability of imperfect label identification schemes resulting in wrong decisions. The expressions are used in computing thresholds and the techniques are given practical applications. The imperfect label identification scheme in the multiclass case is found to amount to establishing a region around each decision surface, and decisions of the label correction scheme are found in close agreement with the analyst-interpreter interpretations of the imagery films. As an example, the application of the maximum likelihood estimation to the processing of Landsat MSS data is discussed.
Chittineni, C. B.↗
On the use of the noncentral chi-square density function for the distribution of helicopter spectral estimates
A probability density function for the variability of ensemble averaged spectral estimates from helicopter acoustic signals in Gaussian background noise was evaluated. Numerical methods for calculating the density function and for determining confidence limits were explored. Density functions were predicted for both synthesized and experimental data and compared with observed spectral estimate variability.
Garber, Donald P.↗
Lightning Characteristics and Lightning Strike Peak Current Probabilities as Related to Aerospace Vehicle Operations
A summary is presented of basic lightning characteristics/criteria for current and future NASA aerospace vehicles. The paper estimates the probability of occurrence of a 200 kA peak lightning return current, should lightning strike an aerospace vehicle in various operational phases, i.e., roll-out, on-pad, launch, reenter/land, and return-to-launch site. A literature search was conducted for previous work concerning occurrence and measurement of peak lighting currents, modeling, and estimating probabilities of launch vehicles/objects being struck by lightning. This paper presents these results.
Johnson, Dale L.↗
Estimation of planetary contamination probabilities due to flight of the U. S. S. R. Venus 3
Estimates of planetary contamination probabilities due to flight of Venus 3
VENUS PROBE↗
On the estimation of a Gaussian convolution probability density.
Gaussian type densities estimated by representing unknown density in terms of convolution expansion of Gaussian probability density and arbitrary distribution, discussing convergence rates
Schwartz, S. C.↗
Approximation of Failure Probability Using Conditional Sampling
In analyzing systems which depend on uncertain parameters, one technique is to partition the uncertain parameter domain into a failure set and its complement, and judge the quality of the system by estimating the probability of failure. If this is done by a sampling technique such as Monte Carlo and the probability of failure is small, accurate approximation can require so many sample points that the computational expense is prohibitive. Previous work of the authors has shown how to bound the failure event by sets of such simple geometry that their probabilities can be calculated analytically. In this paper, it is shown how to make use of these failure bounding sets and conditional sampling within them to substantially reduce the computational burden of approximating failure probability. It is also shown how the use of these sampling techniques improves the confidence intervals for the failure probability estimate for a given number of sample points and how they reduce the number of sample point analyses needed to achieve a given level of confidence.
Giesy. Daniel P.↗
Miserable Risk Estimation: “Managing” Low-Probability, High-Consequence Risks
As with all bold endeavors, NASA missions must identify and manage their risks. While estimating the consequences of these risks is typically straightforward, it can be challenging to estimate their likelihood. This is especially true for low-probability risks, due both to the lack of operational data and the cognitive biases humans bring to risk estimation. In this talk, I will present both a tongue-in-cheek and “Management Science” approach to estimating low-probability, high-consequence risks. I then summon my Human Factors colleagues to provide NASA mission managers with the gentle guidance needed to avoid common errors in risk assessment
Mary K Kaiser↗
Structural reliability methods: Code development status
The Probabilistic Structures Analysis Method (PSAM) program integrates state of the art probabilistic algorithms with structural analysis methods in order to quantify the behavior of Space Shuttle Main Engine structures subject to uncertain loadings, boundary conditions, material parameters, and geometric conditions. An advanced, efficient probabilistic structural analysis software program, NESSUS (Numerical Evaluation of Stochastic Structures Under Stress) was developed as a deliverable. NESSUS contains a number of integrated software components to perform probabilistic analysis of complex structures. A nonlinear finite element module NESSUS/FEM is used to model the structure and obtain structural sensitivities. Some of the capabilities of NESSUS/FEM are shown. A Fast Probability Integration module NESSUS/FPI estimates the probability given the structural sensitivities. A driver module, PFEM, couples the FEM and FPI. NESSUS, version 5.0, addresses component reliability, resistance, and risk.
Millwater, Harry R.↗
New method for estimating low-earth-orbit collision probabilities
An unconventional but general method is described for estimating the probability of collision between an earth-orbiting spacecraft and orbital debris. This method uses a Monte Caralo simulation of the orbital motion of the target spacecraft and each discrete debris object to generate an empirical set of distances, each distance representing the separation between the spacecraft and the nearest debris object at random times. Using concepts from the asymptotic theory of extreme order statistics, an analytical density function is fitted to this set of minimum distances. From this function, it is possible to generate realistic collision estimates for the spacecraft.
Vedder, John D.↗
Rapid and Reliable Damage Proxy Map from InSAR Coherence
Future radar satellites will visit SoCal within a day after a disaster event. Data acquisition latency in 2015-2020 is 8 to approx. 15 hours. Data transfer latency that often involves human/agency intervention far exceeds the data acquisition latency. Need interagency cooperation to establish automatic pipeline for data transfer. The algorithm is tested with ALOS PALSAR data of Pasadena, California. Quantitative quality assessment is being pursued: Meeting with Pasadena City Hall computer engineers for a complete list of demolition/construction project 1. Estimate the probability of detection and probability of false alarm 2. Estimate the optimal threshold value.
InSAR↗
Continuous Habitable Zones: Pairing a GCM and Bayesian Framework to Predict Habitable Zone Evolution
In the near-future, new space telescopes like JWST, LUVOIR, and HabEx will begin attempting to explore the properties of atmospheres of potentially habitable planets. This will require a significant amount of time and resources for even a single planet, which makes it essential to prioritize observations by those most-likely to have detectable life. Here we present a statistical method to estimate the probabilities that specific exoplanets have been continuously in the habitable zone of their host stars for more than 2 billion years, the approximate time it took life on Earth to significantly increase the oxygen content of the atmosphere. We introduce the use of statistics of an ensemble of 3D planetary general circulation models to estimate these probabilities, replacing prior 1D model estimates.
habitable planets↗
Pairing a GCM and Bayesian Framework to Predict Habitable Zone Evolution
In the near-future, new space telescopes like JWST, LUVOIR, and HabEx will begin attempting to explore the properties of atmospheres of potentially habitable planets. This will require a significant amount of time and resources for even a single planet, which makes it essential to prioritize observations by those most-likely to have detectable life. Here we present a statistical method to estimate the probabilities that specific exoplanets have been continuously in the habitable zone of their host stars for more than 2 billion years, the approximate time it took life on Earth to significantly increase the oxygen content of the atmosphere. We introduce the use of statistics of an ensemble of 3D planetary general circulation models to estimate these probabilities, replacing prior 1D model estimates.
habitable planets↗
On the Probability of Occurrence of Extreme Space Weather Events
By virtue of their rarity, extreme space weather events, such as the Carrington event of 1859, are difficult to study, their rates of occurrence are difficult to estimate, and prediction of a specific future event is virtually impossible. Additionally, events may be extreme relative to one parameter but normal relative to others. In this study, we analyze several measures of the severity of space weather events (flare intensity, coronal mass ejection speeds, Dst, and greater than 30 MeV proton fluences as inferred from nitrate records) to estimate the probability of occurrence of extreme events. By showing that the frequency of occurrence scales as an inverse power of the severity of the event, and assuming that this relationship holds at higher magnitudes, we are able to estimate the probability that an event larger than some criteria will occur within a certain interval of time in the future. For example, the probability of another Carrington event (based on Dst less than - 850 nT) occurring within the next decade is approximately 12%. We also identify and address several limitations with this approach. In particular, we assume time stationarity, and thus, the effects of long-term space climate change are not considered. While this technique cannot be used to predict specific events, it may ultimately be useful for probabilistic forecasting.
Riley, Pete↗
A Methodology for Determining Statistical Performance Compliance for Airborne Doppler Radar with Forward-Looking Turbulence Detection Capability
The objective of the research developed and presented in this document was to statistically assess turbulence hazard detection performance employing airborne pulse Doppler radar systems. The FAA certification methodology for forward looking airborne turbulence radars will require estimating the probabilities of missed and false hazard indications under operational conditions. Analytical approaches must be used due to the near impossibility of obtaining sufficient statistics experimentally. This report describes an end-to-end analytical technique for estimating these probabilities for Enhanced Turbulence (E-Turb) Radar systems under noise-limited conditions, for a variety of aircraft types, as defined in FAA TSO-C134. This technique provides for one means, but not the only means, by which an applicant can demonstrate compliance to the FAA directed ATDS Working Group performance requirements. Turbulence hazard algorithms were developed that derived predictive estimates of aircraft hazards from basic radar observables. These algorithms were designed to prevent false turbulence indications while accurately predicting areas of elevated turbulence risks to aircraft, passengers, and crew; and were successfully flight tested on a NASA B757-200 and a Delta Air Lines B737-800. Application of this defined methodology for calculating the probability of missed and false hazard indications taking into account the effect of the various algorithms used, is demonstrated for representative transport aircraft and radar performance characteristics.
Bowles, Roland L.↗
Noiseless compression using non-Markov models
Adaptive data compression techniques can be viewed as consisting of a model specified by a database common to the encoder and decoder, an encoding rule and a rule for updating the model to ensure that the encoder and decoder always agree on the interpretation of the next transmission. The techniques which fit this framework range from run-length coding, to adaptive Huffman and arithmetic coding, to the string-matching techniques of Lempel and Ziv. The compression obtained by arithmetic coding is dependent on the generality of the source model. For many sources, an independent-letter model is clearly insufficient. Unfortunately, a straightforward implementation of a Markov model requires an amount of space exponential in the number of letters remembered. The Directed Acyclic Word Graph (DAWG) can be constructed in time and space proportional to the text encoded, and can be used to estimate the probabilities required for arithmetic coding based on an amount of memory which varies naturally depending on the encoded text. The tail of that portion of the text which was encoded is the longest suffix that has occurred previously. The frequencies of letters following these previous occurrences can be used to estimate the probability distribution of the next letter. Experimental results indicate that compression is often far better than that obtained using independent-letter models, and sometimes also significantly better than other non-independent techniques.
Blumer, Anselm↗
Estimation from incomplete multinomial data
The vector of multinomial cell probabilities was estimated from incomplete data, incomplete in that it contains partially classified observations. Each such partially classified observation was observed to fall in one of two or more selected categories but was not classified further into a single category. The data were assumed to be incomplete at random. The estimation criterion was minimization of risk for quadratic loss. The estimators were the classical maximum likelihood estimate, the Bayesian posterior mode, and the posterior mean. An approximation was developed for the posterior mean. The Dirichlet, the conjugate prior for the multinomial distribution, was assumed for the prior distribution.
Credeur, K. R.↗