SEARCH · Search NASA

Results for “ML”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 379 records · Page 21

All sources NASA NTRS DOE OSTI

DOE OSTI2025

On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning

Quantifying uncertainties for machine learning (ML) models is a foundational challenge in modern data analysis. This challenge is compounded by at least two key aspects of the field: (a) inconsistent terminology surrounding uncertainty and estimation across disciplines, and (b) the varying technical requirements for establishing trustworthy uncertainties in diverse problem contexts. In this position paper, we aim to clarify the depth of these challenges by identifying these inconsistencies and articulating how different contexts impose distinct epistemic demands. We examine the current landscape of estimation targets (e.g., prediction, inference, simulation-based inference), uncertainty constructs (e.g., frequentist, Bayesian, fiducial), and the approaches used to map between them. Drawing on the literature, we highlight and explain examples of problematic mappings. To help address these issues, we advocate for standards that promote alignment between the \textit{intent} and \textit{implementation} of uncertainty quantification (UQ) approaches. We discuss several axes of trustworthiness that are necessary (if not sufficient) for reliable UQ in ML models, and show how these axes can inform the design and evaluation of uncertainty-aware ML systems. Our practical recommendations focus on scientific ML, offering illustrative cases and use scenarios, particularly in the context of simulation-based inference (SBI).

Trivedi, Shubhendu [MIT] (ORCID:0000000312374301)↗

DOE OSTI2026

Thermodynamics-guided machine learning model for predicting convective boundary layer height and its multi-site applicability

Accurate estimation of convective boundary layer height (CBLH) is vital for weather, climate, and air quality modeling. Machine learning (ML) shows promise in CBLH prediction, but input parameter selection often lacks physical grounding, limiting generalizability. This study introduces a novel ML framework for CBLH prediction, integrating thermodynamic constraints and the diurnal CBLH cycle as an implicit physical guide. Boundary layer growth is modeled as driven by surface heat fluxes and atmospheric heat absorption represented with the low tropospheric stability, using the diurnal cycle as input and output. TPOT and AutoKeras are employed to select optimal models, validated against Doppler lidar-derived CBLH data, achieving an R 2 of 0.84 across untrained years. Comparisons of eddy covariance (ECOR) and energy balance Bowen ratio (EBBR) flux measurements show the same prediction capability. Models trained on the ARM SGP C1 site with ECOR data and tested at E37 and E39 yield R 2 values of 0.79 and 0.81, respectively, demonstrating their adaptability. The ML model trained with all sites' data slightly enhances the performance compared with ML models trained over single-site data. The interquartile range for predicted CBLH is consistently narrower than that for DL-derived CBLH, reflecting lower variability in predicted CBLH compared to DL-derived CBLH, which is influenced by additional factors, which are not well represented with the model inputs. The model's generalizability across multiple sites at the ARM SGP site demonstrates its potential for transfer to greater distances, offering a scalable approach for enhancing boundary layer parameterization in atmospheric models.

Chu, Yufei [Stony Brook Univ., NY (United States)]↗

DOE OSTI2025

ELM2.1-XGBfire1.0: improving wildfire prediction by integrating a machine learning fire model in a land surface model

Wildfires have shown increasing trends in both frequency and severity across the contiguous United States (CONUS). However, process-based fire models have difficulties in accurately simulating the burned area over the CONUS due to a simplification of the physical process and cannot capture the interplay among fire, ignition, climate, and human activities. The deficiency of burned area simulation deteriorates the description of fire impact on energy balance, water budget, and carbon fluxes in the Earth system models (ESMs). Alternatively, fire models based on machine learning (ML), which capture statistical relationships between the burned area and environmental factors, have shown promising burned area predictions and corresponding fire impact simulation. We develop a hybrid framework (ELM2.1-XGBFire1.0) that integrates an eXtreme Gradient Boosting (XGBoost) wildfire model with the Energy Exascale Earth System Model (E3SM) land model (ELM) version 2.1. A Fortran–C–Python deep learning bridge is adapted to support online communication between ELM and the ML fire model. Specifically, the burned area predicted by the ML-based wildfire model is directly passed to ELM to adjust the carbon pool and vegetation dynamics after disturbance, which are then used as predictors in the ML-based fire model in the next time step. Evaluated against the historical burned area from Global Fire Emissions Database 5 from 2001–2019, the ELM2.1-XGBFire1.0 outperforms process-based fire models in terms of spatial distribution and seasonal variations. The ELM2.1-XGBFire1.0 has proven to be a new tool for studying vegetation–fire interactions and, more importantly, enables seamless exploration of climate–fire feedback, working as an active component of E3SM.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1974

Maximum likelihood identification of aircraft stability and control derivatives

Application of a generalized identification method to flight test data analysis. The method is based on the maximum likelihood (ML) criterion and includes output error and equation error methods as special cases. Both the linear and nonlinear models with and without process noise are considered. The flight test data from lateral maneuvers of HL-10 and M2/F3 lifting bodies are processed to determine the lateral stability and control derivatives, instrumentation accuracies, and biases. A comparison is made between the results of the output error method and the ML method for M2/F3 data containing gusts. It is shown that better fits to time histories are obtained by using the ML method. The nonlinear model considered corresponds to the longitudinal equations of the X-22 VTOL aircraft. The data are obtained from a computer simulation and contain both process and measurement noise. The applicability of the ML method to nonlinear models with both process and measurement noise is demonstrated.

Mehra, R. K.↗

NASA NTRS1982

A comparison of minimum distance and maximum likelihood techniques for proportion estimation

The estimation of mixing proportions P sub 1, P sub 2,...P sub m in the mixture density f(x) = the sum of the series P sub i F sub i(X) with i = 1 to M is often encountered in agricultural remote sensing problems in which case the p sub i's usually represent crop proportions. In these remote sensing applications, component densities f sub i(x) have typically been assumed to be normally distributed, and parameter estimation has been accomplished using maximum likelihood (ML) techniques. Minimum distance (MD) estimation is examined as an alternative to ML where, in this investigation, both procedures are based upon normal components. Results indicate that ML techniques are superior to MD when component distributions actually are normal, while MD estimation provides better estimates than ML under symmetric departures from normality. When component distributions are not symmetric, however, it is seen that neither of these normal based techniques provides satisfactory results.

Woodward, W. A.↗

NASA NTRS1993

A robust multilevel simultaneous eigenvalue solver

Multilevel (ML) algorithms for eigenvalue problems are often faced with several types of difficulties such as: the mixing of approximated eigenvectors by the solution process, the approximation of incomplete clusters of eigenvectors, the poor representation of solution on coarse levels, and the existence of close or equal eigenvalues. Algorithms that do not treat appropriately these difficulties usually fail, or their performance degrades when facing them. These issues motivated the development of a robust adaptive ML algorithm which treats these difficulties, for the calculation of a few eigenvectors and their corresponding eigenvalues. The main techniques used in the new algorithm include: the adaptive completion and separation of the relevant clusters on different levels, the simultaneous treatment of solutions within each cluster, and the robustness tests which monitor the algorithm's efficiency and convergence. The eigenvectors' separation efficiency is based on a new ML projection technique generalizing the Rayleigh Ritz projection, combined with a technique, the backrotations. These separation techniques, when combined with an FMG formulation, in many cases lead to algorithms of O(qN) complexity, for q eigenvectors of size N on the finest level. Previously developed ML algorithms are less focused on the mentioned difficulties. Moreover, algorithms which employ fine level separation techniques are of O(q(sub 2)N) complexity and usually do not overcome all these difficulties. Computational examples are presented where Schrodinger type eigenvalue problems in 2-D and 3-D, having equal and closely clustered eigenvalues, are solved with the efficiency of the Poisson multigrid solver. A second order approximation is obtained in O(qN) work, where the total computational work is equivalent to only a few fine level relaxations per eigenvector.

Costiner, Sorin↗

NASA NTRS1994

Microbiological characterization of a regenerative life support system

A Variable Pressure Plant Growth Chamber (VPGC), at the Johnson Space Center's (JSC) ground-based Regenerative Life Support Systems (RLSS) test bed, was used to produce crops of soil-grown lettuce. The crops and chamber were analyzed for microbiological diversity during lettuce growth and after harvest. Bacterial counts for the rhizosphere, spent nutrient medium, heat exchanger condensate, and atmosphere were approximately 1011 Colony Forming Units (CFU) g −1 , 105 CFU ml −1 , 105 CFU ml −1 , and 600 CFU m −3 , respectively. Pseudomonas was the predominant bacterial genus. Numbers of fungi were about 105 CFU g −1 in the rhizosphere, 4–200 CFU ml −1 in the spent nutrient medium, 110 CFU ml −1 in the heat exchanger condensate, and 3 CFU m −3 in the atmosphere. Fusarium and Trichoderma were the predominant fungal genera.

D W Koenig↗

NASA NTRS1997

Soft-Decision Decoding of Binary Linear Block Codes Based on an Iterative Search Algorithm

This correspondence presents a suboptimum soft-decision decoding scheme for binary linear block codes based on an iterative search algorithm. The scheme uses an algebraic decoder to iteratively generate a sequence of candidate codewords one at a time using a set of test error patterns that are constructed based on the reliability information of the received symbols. When a candidate codeword is generated, it is tested based on an optimality condition. If it satisfies the optimality condition, then it is the most likely (ML) codeword and the decoding stops. If it fails the optimality test, a search for the ML codeword is conducted in a region which contains the ML codeword. The search region is determined by the current candidate codeword and the reliability of the received symbols. The search is conducted through a purged trellis diagram for the given code using the Viterbi algorithm. If the search fails to find the ML codeword, a new candidate is generated using a new test error pattern, and the optimality test and search are renewed. The process of testing and search continues until either the MEL codeword is found or all the test error patterns are exhausted and the decoding process is terminated. Numerical results show that the proposed decoding scheme achieves either practically optimal performance or a performance only a fraction of a decibel away from the optimal maximum-likelihood decoding with a significant reduction in decoding complexity compared with the Viterbi decoding based on the full trellis diagram of the codes.

Lin, Shu↗

NASA NTRS2002

Statistical Properties of Maximum Likelihood Estimators of Power Law Spectra Information

A simple power law model consisting of a single spectral index, a is believed to be an adequate description of the galactic cosmic-ray (GCR) proton flux at energies below 10(exp 13) eV, with a transition at the knee energy, E(sub k), to a steeper spectral index alpha(sub 2) greater than alpha(sub 1) above E(sub k). The Maximum likelihood (ML) procedure was developed for estimating the single parameter alpha(sub 1) of a simple power law energy spectrum and generalized to estimate the three spectral parameters of the broken power law energy spectrum from simulated detector responses and real cosmic-ray data. The statistical properties of the ML estimator were investigated and shown to have the three desirable properties: (P1) consistency (asymptotically unbiased). (P2) efficiency asymptotically attains the Cramer-Rao minimum variance bound), and (P3) asymptotically normally distributed, under a wide range of potential detector response functions. Attainment of these properties necessarily implies that the ML estimation procedure provides the best unbiased estimator possible. While simulation studies can easily determine if a given estimation procedure provides an unbiased estimate of the spectra information, and whether or not the estimator is approximately normally distributed, attainment of the Cramer-Rao bound (CRB) can only he ascertained by calculating the CRB for an assumed energy spectrum-detector response function combination, which can be quite formidable in practice. However. the effort in calculating the CRB is very worthwhile because it provides the necessary means to compare the efficiency of competing estimation techniques and, furthermore, provides a stopping rule in the search for the best unbiased estimator. Consequently, the CRB for both the simple and broken power law energy spectra are derived herein and the conditions under which they are attained in practice are investigated. The ML technique is then extended to estimate spectra information from an arbitrary number of astrophysics data sets produced by vastly different science instruments. This theory and its successful implementation will facilitate the interpretation of spectral information from multiple astrophysics missions and thereby permit the derivation of superior spectral parameter estimates based on the combination of data sets.

Howell, L. W.↗

NASA NTRS2003

Importance of mitral valve repair associated with left ventricular reconstruction for patients with ischemic cardiomyopathy: a real-time three-dimensional echocardiographic study

BACKGROUND: Left ventricular (LV) reconstruction surgery leads to early improvement in LV function in ischemic cardiomyopathy (ICM) patients. This study was designed to evaluate the impact of mitral valve (MV) repair associated with LV reconstruction on LV function 1-year after surgery in ICM patients assessed by real-time 3-dimensional echocardiography (3DE). METHODS AND RESULTS: Sixty ICM patients who underwent the combination surgery (LV reconstruction in 60, MV repair in 30, and revascularization in 52 patients) were studied. Real-time 3DE was performed and LV volumes were obtained at baseline, discharge, 6-month and >or=12-month follow-up. Reduction in end-diastolic volumes (EDV) by 29% and in end-systolic volumes by 38% were demonstrated immediately after surgery and remained at subsequent follow-up (P<0.0001). The LV ejection fraction significantly increased by about 10% at discharge and was maintained >or=12-month (P<0.0001). Although the LV volumes were significantly larger in patients with MV repair before surgery (EDV, 235+/-87 mL versus 193+/-67 mL, P<0.05), they were similar to LV volumes of the patients without MV repair at subsequent follow-ups. However, the EDV increased from 139+/-24 mL to 227+/-79 mL (P<0.01) in 7 patients with recurrent mitral regurgitation (MR). Improvement in New York Heart Association functional class occurred in 81% patients during late follow-up. CONCLUSIONS: Real-time 3DE demonstrates that LV reconstruction provides significant reduction in LV volumes and improvement in LV function which is sustained throughout the 1-year follow-up with 84% cardiac event free survival. If successful, MV repair may prevent LV redilation, while recurrent MR is associated with increased LV volumes.

Clinical Trial↗

NASA NTRS2001

Effects of spaceflight on human calf hemodynamics

Chronic microgravity may modify adaptations of the leg circulation to gravitational pressures. We measured resting calf compliance and blood flow with venous occlusion plethysmography, and arterial blood pressure with sphygmomanometry, in seven subjects before, during, and after spaceflight. Calf vascular resistance equaled mean arterial pressure divided by calf flow. Compliance equaled the slope of the calf volume change and venous occlusion pressure relationship for thigh cuff pressures of 20, 40, 60, and 80 mmHg held for 1, 2, 3, and 4 min, respectively, with 1-min breaks between occlusions. Calf blood flow decreased 41% in microgravity (to 1.15 +/- 0.16 ml x 100 ml(-1) x min(-1)) relative to 1-G supine conditions (1.94 +/- 0.19 ml x 100 ml(-1) x min(-1), P = 0.01), and arterial pressure tended to increase (P = 0.05), such that calf vascular resistance doubled in microgravity (preflight: 43 +/- 4 units; in-flight: 83 +/- 13 units; P < 0.001) yet returned to preflight levels after flight. Calf compliance remained unchanged in microgravity but tended to increase during the first week postflight (P > 0.2). Calf vasoconstriction in microgravity qualitatively agrees with the "upright set-point" hypothesis: the circulation seeks conditions approximating upright posture on Earth. No calf hemodynamic result exhibited obvious mechanistic implications for postflight orthostatic intolerance.

NASA Experiment Number 178294 2/2↗

NASA NTRS1998

Albumin infusion in humans does not model exercise induced hypervolaemia after 24 hours

We rapidly infused 234 +/- 3 mL of 5% human serum albumin in eight men while measuring haematocrit, haemoglobin concentration, plasma volume (PV), albumin concentration, total protein concentration, osmolality, sodium concentration, renin activity, aldosterone concentration, and atrial natriuretic peptide concentration to test the hypotheses that plasma volume expansion and plasma albumin content expansion will not persist for 24 h. Plasma volume and albumin content were expanded for the first 6 h after infusion (44.3 +/- 1.9-47.2 +/- 2.0 mL kg-1 and 1.9 +/- 0.1-2.1 +/- 0.1 g kg-1 at pre-infusion and 1 h, respectively, P < 0.05), but by 24 h plasma volume and albumin content decreased significantly from 1 h post-infusion and were not different from pre-infusion (44.8 +/- 1.9 mL kg-1 and 1.9 +/- 0.1 g kg-1, respectively). Plasma aldosterone concentration showed a significant effect of time over the 24 h after infusion (P < 0.05), and showed a trend to decrease at 2 h after infusion (167.6 +/- 32.5(-1) 06.2 +/- 13.4 pg mL-1, P = 0.07). These data demonstrate that a 6.8% expansion of plasma volume and 10.5% expansion of plasma albumin content by infusion does not remain in the vascular space for 24 h and suggest a redistribution occurs between the intravascular space and interstitial fluid space.

Non-NASA Center↗

NASA NTRS1998

Changes in peak oxygen uptake and plasma volume in fit and unfit subjects following exposure to a simulation of microgravity

To test the hypothesis that the magnitude of reduction in plasma volume and work capacity following exposure to simulated microgravity is dependent on the initial level of aerobic fitness, peak oxygen uptake (VO2peak) was measured in a group of physically fit subjects and compared with VO2peak in a group of relatively unfit subjects before and after 10 days of continuous 6 degrees head-down tilt (HDT). Ten fit subjects (40 +/- 2 year) with mean +/- SE VO2peak = 48.9 +/- 1.7 mL kg-1 min-1 were matched for age, height, and lean body weight with 10 unfit subjects (VO2peak = 37.7 +/- 1.6 mL kg-1 min-1). Before and after HDT, plasma, blood, and red cell volumes and body composition were measured and all subjects underwent a graded supine cycle ergometer test to determine VO2peak period needed. Reduced VO2peak in fit subjects (-16.2%) was greater than that of unfit subjects (-6.1%). Similarly, reductions in plasma (-18.3%) and blood volumes (-16.0%) in fit subjects were larger than those of unfit subjects (blood volume = -5.6%; plasma volume = -6.6%). Reduced plasma volume was associated with greater negative body fluid balance during the initial 24 h of HDT in the fit group (912 +/- 154 mL) compared with unfit subjects (453 +/- 200 mL). The percentage change for VO2peak correlated with percentage change in plasma volume (r = +0.79). Following exposure to simulated microgravity, fit subjects demonstrated larger reductions in VO2peak than unfit subjects which was associated with larger reductions in plasma and blood volume. These data suggest that the magnitude of physical deconditioning induced by exposure to microgravity without intervention of countermeasures was influenced by the initial fitness of the subjects.

Non-NASA Center↗

NASA NTRS1998

Restoration of peak vascular conductance after simulated microgravity by maximal exercise

We sought to determine if (i) peak vascular conductance of the calf was reduced following prolonged exposure to simulated microgravity, and (ii) if maximal cycle ergometry performed at the end of microgravity exposure stimulated a restoration of peak calf vascular conductance. To do this, peak vascular conductance of the calf was recorded following ischaemic plantar flexion exercise to fatigue in seven men after 16 days of head-down tilt (HDT) under two conditions: (i) after one bout of maximal supine cycle ergometry completed 24 h prior to performance of ischaemic plantar flexion exercise, and (ii) in a control (no cycle ergometry) condition. Following HDT, peak vascular conductance was reduced in the control condition (0.38 +/- 0.02 to 0.24 +/- 0.02 ml 100 ml-1 min-1 mmHg-1; P = 0.04), but was restored when subjects performed cycle ergometry (0.33 +/- 0.05 to 0.28 +/- 0.04 ml 100 ml-1 min-1 mmHg-1; P = 0.46). After HDT, time to fatigue during ischaemic plantar flexion exercise was not different from pre-HDT 24 h after performance of exhaustive cycle ergometry (120 +/- 24 vs. 122 +/- 19 s), but was decreased in the control condition (116 +/- 11 vs. 95 +/- 8 s; P = 0.07). These data suggest that a single bout of maximal exercise can provide a stimulus to restore peak vascular conductance and maintain time to fatigue during performance of ischaemic plantar flexion exercise.

NASA Discipline Cardiopulmonary↗

NASA NTRS2007

Mechanisms Controlling the Interannual Variation of Mixed Layer Temperature Averaged over the Nino-3 Region

The present study examines processes governing the interannual variation of MLT in the eastern equatorial Pacific.Processes controlling the interannual variation of mixed layer temperature (MLT) averaged over the Nino-3 domain (5 deg N-5 deg S, 150 deg-90 deg W) are studied using an ocean data assimilation product that covers the period of 1993-2003. The overall balance is such that surface heat flux opposes the MLT change but horizontal advection and subsurface processes assist the change. Advective tendencies are estimated here as the temperature fluxes through the domain's boundaries, with the boundary temperature referenced to the domain-averaged temperature to remove the dependence on temperature scale. This allows the authors to characterize external advective processes that warm or cool the water within the domain as a whole. The zonal advective tendency is caused primarily by large-scale advection of warm-pool water through the western boundary of the domain. The meridional advective tendency is contributed to mostly by Ekman current advecting large-scale temperature anomalies through the southern boundary of the domain. Unlike many previous studies, the subsurface processes that consist of vertical mixing and entrainment are explicitly evaluated. In particular, a rigorous method to estimate entrainment allows an exact budget closure. The vertical mixing across the mixed layer (ML) base has a contribution in phase with the MLT change. The entrainment tendency due to the temporal change in ML depth is negligible compared to other subsurface processes. The entrainment tendency by vertical advection across the ML base is dominated by large-scale changes in upwelling and the temperature of upwelling water. Tropical instability waves (TIWs) result in smaller-scale vertical advection that warms the domain during La Nina cooling events. However, such a warming tendency is overwhelmed by the cooling tendency associated with the large-scale upwelling by a factor of 2. In summary, all the balance terms are important in the MLT budget except the entrainment due to lateral induction and temporal variation in ML depth. All three advective tendencies are primarily caused by large-scale and low-frequency processes, and they assist the Nino-3 MLT change.

eastern equatorial Pacific↗

NASA NTRS2010

Maximum Likelihood Time-of-Arrival Estimation of Optical Pulses via Photon-Counting Photodetectors

Many optical imaging, ranging, and communications systems rely on the estimation of the arrival time of an optical pulse. Recently, such systems have been increasingly employing photon-counting photodetector technology, which changes the statistics of the observed photocurrent. This requires time-of-arrival estimators to be developed and their performances characterized. The statistics of the output of an ideal photodetector, which are well modeled as a Poisson point process, were considered. An analytical model was developed for the mean-square error of the maximum likelihood (ML) estimator, demonstrating two phenomena that cause deviations from the minimum achievable error at low signal power. An approximation was derived to the threshold at which the ML estimator essentially fails to provide better than a random guess of the pulse arrival time. Comparing the analytic model performance predictions to those obtained via simulations, it was verified that the model accurately predicts the ML performance over all regimes considered. There is little prior art that attempts to understand the fundamental limitations to time-of-arrival estimation from Poisson statistics. This work establishes both a simple mathematical description of the error behavior, and the associated physical processes that yield this behavior. Previous work on mean-square error characterization for ML estimators has predominantly focused on additive Gaussian noise. This work demonstrates that the discrete nature of the Poisson noise process leads to a distinctly different error behavior.

Erkmen, Baris I.↗

NASA NTRS2006

Less-Complex Method of Classifying MPSK

An alternative to an optimal method of automated classification of signals modulated with M-ary phase-shift-keying (M-ary PSK or MPSK) has been derived. The alternative method is approximate, but it offers nearly optimal performance and entails much less complexity, which translates to much less computation time. Modulation classification is becoming increasingly important in radio-communication systems that utilize multiple data modulation schemes and include software-defined or software-controlled receivers. Such a receiver may "know" little a priori about an incoming signal but may be required to correctly classify its data rate, modulation type, and forward error-correction code before properly configuring itself to acquire and track the symbol timing, carrier frequency, and phase, and ultimately produce decoded bits. Modulation classification has long been an important component of military interception of initially unknown radio signals transmitted by adversaries. Modulation classification may also be useful for enabling cellular telephones to automatically recognize different signal types and configure themselves accordingly. The concept of modulation classification as outlined in the preceding paragraph is quite general. However, at the present early stage of development, and for the purpose of describing the present alternative method, the term "modulation classification" or simply "classification" signifies, more specifically, a distinction between M-ary and M'-ary PSK, where M and M' represent two different integer multiples of 2. Both the prior optimal method and the present alternative method require the acquisition of magnitude and phase values of a number (N) of consecutive baseband samples of the incoming signal + noise. The prior optimal method is based on a maximum- likelihood (ML) classification rule that requires a calculation of likelihood functions for the M and M' hypotheses: Each likelihood function is an integral, over a full cycle of carrier phase, of a complicated sum of functions of the baseband sample values, the carrier phase, the carrier-signal and noise magnitudes, and M or M'. Then the likelihood ratio, defined as the ratio between the likelihood functions, is computed, leading to the choice of whichever hypothesis - M or M'- is more likely. In the alternative method, the integral in each likelihood function is approximated by a sum over values of the integrand sampled at a number, 1, of equally spaced values of carrier phase. Used in this way, 1 is a parameter that can be adjusted to trade computational complexity against the probability of misclassification. In the limit as 1 approaches infinity, one obtains the integral form of the likelihood function and thus recovers the ML classification. The present approximate method has been tested in comparison with the ML method by means of computational simulations. The results of the simulations have shown that the performance (as quantified by probability of misclassification) of the approximate method is nearly indistinguishable from that of the ML method (see figure).

Hamkins, Jon↗

NASA NTRS2011

Extensions to the Dynamic Aerospace Vehicle Exchange Markup Language

The Dynamic Aerospace Vehicle Exchange Markup Language (DAVE-ML) is a syntactical language for exchanging flight vehicle dynamic model data. It provides a framework for encoding entire flight vehicle dynamic model data packages for exchange and/or long-term archiving. Version 2.0.1 of DAVE-ML provides much of the functionality envisioned for exchanging aerospace vehicle data; however, it is limited in only supporting scalar time-independent data. Additional functionality is required to support vector and matrix data, abstracting sub-system models, detailing dynamics system models (both discrete and continuous), and defining a dynamic data format (such as time sequenced data) for validation of dynamics system models and vehicle simulation packages. Extensions to DAVE-ML have been proposed to manage data as vectors and n-dimensional matrices, and record dynamic data in a compatible form. These capabilities will improve the clarity of data being exchanged, simplify the naming of parameters, and permit static and dynamic data to be stored using a common syntax within a single file; thereby enhancing the framework provided by DAVE-ML for exchanging entire flight vehicle dynamic simulation models.

Brian, Geoffrey J.↗