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Results for “Maximum Likelihood Estimation”

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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
NASA NTRS1997

Biweekly Maps of Wind Stress for the North Pacific from the ERS-1 Scatterometer

The European Remote-sensing Satellite (ERS-1) was launched in July 1991 and contained several instruments for observing the Earth's ocean including a wind scatterometer. The scatterometer measurements were processed by the European Space Agency (ESA) and the Jet Propulsion Laboratory (JPL). JPL reprocessed (Freilich and Dunbar, 1992) the ERS-1 backscatter measurements to produced a 'value added' data set that contained the ESA wind vector as well as a set of up to four ambiguities. These ambiguities were further processed using a maximum-likelihood estimation (MLE) and a median filter to produce a 'selected vector.' This report describes a technique developed to produce time-averaged wind field estimates with their expected errors using only scatterometer wind vectors. The processing described in this report involved extracting regions of interest from the data tapes, checking the quality and creating the wind field estimate. This analysis also includes the derivation of biweekly average wind vectors over the North Pacific Ocean at a resolution of 0.50 x 0.50. This was done with an optimal average algorithm temporally and an over-determined biharmonic spline spatially. There have been other attempts at creating gridded wind files from ERS-1 winds, e.g., kriging techniques (Bentamy et al., 1996) and successive corrections schemes (Tang and Liu, 1996). There are several inherent problems with the ERS-1 scatterometer. Since this is a multidisciplinary mission, the satellite is flown in different orbits optimized for each phase of the mission. The scatterometer also shares several sub-systems with the Synthetic Aperture Radar (SAR) and cannot be operated while the SAR is in operation. The scatterometer is also a single-sided instrument and only measures backscatter along the right side of the satellite. The processing described here generates biweekly wind maps during the wktwo years analysis period regardless of the satellite orbit or missing data.

SYNTHETIC APERTURE RADAR↗
NASA NTRS1999

Distant Massive Clusters and Cosmology

We present a status report of our X-ray study and analysis of a complete sample of distant (z=0.5-0.8), X-ray luminous clusters of galaxies. We have obtained ASCA and ROSAT observations of the five brightest Extended Medium Sensitivity (EMSS) clusters with z > 0.5. We have constructed an observed temperature function for these clusters, and measured iron abundances for all of these clusters. We have developed an analytic expression for the behavior of the mass-temperature relation in a low-density universe. We use this mass-temperature relation together with a Press-Schechter-based model to derive the expected temperature function for different values of Omega-M. We combine this analysis with the observed temperature functions at redshifts from 0 - 0.8 to derive maximum likelihood estimates for the value of Omega-M. We report preliminary results of this analysis.

Donahue, Megan↗
NASA NTRS2000

The GEOS Ozone Data Assimilation System: Specification of Error Statistics

A global three-dimensional ozone data assimilation system has been developed at the Data Assimilation Office of the NASA/Goddard Space Flight Center. The Total Ozone Mapping Spectrometer (TOMS) total ozone and the Solar Backscatter Ultraviolet (SBUV) or (SBUV/2) partial ozone profile observations are assimilated. The assimilation, into an off-line ozone transport model, is done using the global Physical-space Statistical Analysis Scheme (PSAS). This system became operational in December 1999. A detailed description of the statistical analysis scheme, and in particular, the forecast and observation error covariance models is given. A new global anisotropic horizontal forecast error correlation model accounts for a varying distribution of observations with latitude. Correlations are largest in the zonal direction in the tropics where data is sparse. Forecast error variance model is proportional to the ozone field. The forecast error covariance parameters were determined by maximum likelihood estimation. The error covariance models are validated using x squared statistics. The analyzed ozone fields in the winter 1992 are validated against independent observations from ozone sondes and HALOE. There is better than 10% agreement between mean Halogen Occultation Experiment (HALOE) and analysis fields between 70 and 0.2 hPa. The global root-mean-square (RMS) difference between TOMS observed and forecast values is less than 4%. The global RMS difference between SBUV observed and analyzed ozone between 50 and 3 hPa is less than 15%.

Stajner, Ivanka↗
NASA NTRS1995

Reliability Stress-Strength Models for Dependent Observations with Applications in Clinical Trials

We consider the applications of stress-strength models in studies involving clinical trials. When studying the effects and side effects of certain procedures (treatments), it is often the case that observations are correlated due to subject effect, repeated measurements and observing many characteristics simultaneously. We develop maximum likelihood estimator (MLE) and uniform minimum variance unbiased estimator (UMVUE) of the reliability which in clinical trial studies could be considered as the chances of increased side effects due to a particular procedure compared to another. The results developed apply to both univariate and multivariate situations. Also, for the univariate situations we develop simple to use lower confidence bounds for the reliability. Further, we consider the cases when both stress and strength constitute time dependent processes. We define the future reliability and obtain methods of constructing lower confidence bounds for this reliability. Finally, we conduct simulation studies to evaluate all the procedures developed and also to compare the MLE and the UMVUE.

Kushary, Debashis↗
NASA NTRS1996

Two-stage model of radon-induced malignant lung tumors in rats: effects of cell killing

A two-stage stochastic model of carcinogenesis is used to analyze lung tumor incidence in 3750 rats exposed to varying regimens of radon carried on a constant-concentration uranium ore dust aerosol. New to this analysis is the parameterization of the model such that cell killing by the alpha particles could be included. The model contains parameters characterizing the rate of the first mutation, the net proliferation rate of initiated cells, the ratio of the rates of cell loss (cell killing plus differentiation) and cell division, and the lag time between the appearance of the first malignant cell and the tumor. Data analysis was by standard maximum likelihood estimation techniques. Results indicate that the rate of the first mutation is dependent on radon and consistent with in vitro rates measured experimentally, and that the rate of the second mutation is not dependent on radon. An initial sharp rise in the net proliferation rate of initiated cell was found with increasing exposure rate (denoted model I), which leads to an unrealistically high cell-killing coefficient. A second model (model II) was studied, in which the initial rise was attributed to promotion via a step function, implying that it is due not to radon but to the uranium ore dust. This model resulted in values for the cell-killing coefficient consistent with those found for in vitro cells. An "inverse dose-rate" effect is seen, i.e. an increase in the lifetime probability of tumor with a decrease in exposure rate. This is attributed in large part to promotion of intermediate lesions. Since model II is preferable on biological grounds (it yields a plausible cell-killing coefficient), such as uranium ore dust. This analysis presents evidence that a two-stage model describes the data adequately and generates hypotheses regarding the mechanism of radon-induced carcinogenesis.

NASA Program Radiation Health↗
NASA NTRS2005

The Influence of Roughness on Gear Surface Fatigue

Gear working surfaces are subjected to repeated rolling and sliding contacts, and often designs require loads sufficient to cause eventual fatigue of the surface. This research provides experimental data and analytical tools to further the understanding of the causal relationship of gear surface roughness to surface fatigue. The research included evaluations and developments of statistical tools for gear fatigue data, experimental evaluation of the surface fatigue lives of superfinished gears with a near-mirror quality, and evaluations of the experiments by analytical methods and surface inspections. Alternative statistical methods were evaluated using Monte Carlo studies leading to a final recommendation to describe gear fatigue data using a Weibull distribution, maximum likelihood estimates of shape and scale parameters, and a presumed zero-valued location parameter. A new method was developed for comparing two datasets by extending the current methods of likelihood-ratio based statistics. The surface fatigue lives of superfinished gears were evaluated by carefully controlled experiments, and it is shown conclusively that superfinishing of gears can provide for significantly greater lives relative to ground gears. The measured life improvement was approximately a factor of five. To assist with application of this finding to products, the experimental condition was evaluated. The fatigue life results were expressed in terms of specific film thickness and shown to be consistent with bearing data. Elastohydrodynamic and stress analyses were completed to relate the stress condition to fatigue. Smooth-surface models do not adequately explain the improved fatigue lives. Based on analyses using a rough surface model, it is concluded that the improved fatigue lives of superfinished gears is due to a reduced rate of near-surface micropitting fatigue processes, not due to any reduced rate of spalling (sub-surface) fatigue processes. To complete the evaluations, surface inspection were completed. The surface topographies of the ground gears changed substantially due to running, but the topographies of the superfinished gears were essentially unchanged with running.

Krantz, Timothy↗
NASA NTRS2005

Statistical Considerations of Data Processing in Giovanni Online Tool

The GES DISC Interactive Online Visualization and Analysis Infrastructure (Giovanni) is a web-based interface for the rapid visualization and analysis of gridded data from a number of remote sensing instruments. The GES DISC currently employs several Giovanni instances to analyze various products, such as Ocean-Giovanni for ocean products from SeaWiFS and MODIS-Aqua; TOMS & OM1 Giovanni for atmospheric chemical trace gases from TOMS and OMI, and MOVAS for aerosols from MODIS, etc. (http://giovanni.gsfc.nasa.gov) Foremost among the Giovanni statistical functions is data averaging. Two aspects of this function are addressed here. The first deals with the accuracy of averaging gridded mapped products vs. averaging from the ungridded Level 2 data. Some mapped products contain mean values only; others contain additional statistics, such as number of pixels (NP) for each grid, standard deviation, etc. Since NP varies spatially and temporally, averaging with or without weighting by NP will be different. In this paper, we address differences of various weighting algorithms for some datasets utilized in Giovanni. The second aspect is related to different averaging methods affecting data quality and interpretation for data with non-normal distribution. The present study demonstrates results of different spatial averaging methods using gridded SeaWiFS Level 3 mapped monthly chlorophyll a data. Spatial averages were calculated using three different methods: arithmetic mean (AVG), geometric mean (GEO), and maximum likelihood estimator (MLE). Biogeochemical data, such as chlorophyll a, are usually considered to have a log-normal distribution. The study determined that differences between methods tend to increase with increasing size of a selected coastal area, with no significant differences in most open oceans. The GEO method consistently produces values lower than AVG and MLE. The AVG method produces values larger than MLE in some cases, but smaller in other cases. Further studies indicated that significant differences between AVG and MLE methods occurred in coastal areas where data have large spatial variations and a log-bimodal distribution instead of log-normal distribution.

Suhung, Shen↗
NASA NTRS2002

Detecting ocean surface wind using the TRMM precipitation radar

Although designed specifically for the measurement of precipitation in the atmosphere, the Precipitation Radar (PR) onboard the Tropical Rainfall Measuring Mission (TRMM) satellite alsomeasures the normalized radar cross-section at the Earth's surface. As such, this instrument providesan interesting opportunity to explore an alternative radar configuration in the satellite remote sensingnf ocean winds. In particular, it can measure the strength of the ocean winds in relatively higherspatial resolution as compared to the conventional scatterometer systems presently in use. The addition of wind sensing capability to precipitation radar also complements its rain profiling capabilitynicely and allows coincident wind and rain measurements. In this paper, we discuss the new ocean wind algorithm developed specifically for TRMM PR using maximum likelihood estimation. A uniquefeature of this wind algorithm is its capability to incorporate measurement sensitivity and noiseinformation consistently in both the along-track and cross-track directions. This PR wind algorithm hasbeen tested using data from the TRMM Microwave Imager (TMI) and NASA QuikSCAT scatterometer.Excellent agreement on the retrieved wind strengths is achieved among three sensors.

precipitation radar TRMM GPM CAMEX-4 ocean surface↗
NASA NTRS1998

A Probabilistic Formulation for Hausdorff Matching

Matching images based on a Hausdorff measure has become popular for computer vision applications. In this paper, we develope a probabilistic formulation for Hausdorff matching in terms of maximum likelihood estimation.

hausdorff Matching↗
NASA NTRS2008

Processing LiDAR Data to Predict Natural Hazards

ELF-Base and ELF-Hazards (wherein 'ELF' signifies 'Extract LiDAR Features' and 'LiDAR' signifies 'light detection and ranging') are developmental software modules for processing remote-sensing LiDAR data to identify past natural hazards (principally, landslides) and predict future ones. ELF-Base processes raw LiDAR data, including LiDAR intensity data that are often ignored in other software, to create digital terrain models (DTMs) and digital feature models (DFMs) with sub-meter accuracy. ELF-Hazards fuses raw LiDAR data, data from multispectral and hyperspectral optical images, and DTMs and DFMs generated by ELF-Base to generate hazard risk maps. Advanced algorithms in these software modules include line-enhancement and edge-detection algorithms, surface-characterization algorithms, and algorithms that implement innovative data-fusion techniques. The line-extraction and edge-detection algorithms enable users to locate such features as faults and landslide headwall scarps. Also implemented in this software are improved methodologies for identification and mapping of past landslide events by use of (1) accurate, ELF-derived surface characterizations and (2) three LiDAR/optical-data-fusion techniques: post-classification data fusion, maximum-likelihood estimation modeling, and hierarchical within-class discrimination. This software is expected to enable faster, more accurate forecasting of natural hazards than has previously been possible.

Fairweather, Ian↗
NASA NTRS2008

Dynamic Method for Identifying Collected Sample Mass

G-Sample is designed for sample collection missions to identify the presence and quantity of sample material gathered by spacecraft equipped with end effectors. The software method uses a maximum-likelihood estimator to identify the collected sample's mass based on onboard force-sensor measurements, thruster firings, and a dynamics model of the spacecraft. This makes sample mass identification a computation rather than a process requiring additional hardware. Simulation examples of G-Sample are provided for spacecraft model configurations with a sample collection device mounted on the end of an extended boom. In the absence of thrust knowledge errors, the results indicate that G-Sample can identify the amount of collected sample mass to within 10 grams (with 95-percent confidence) by using a force sensor with a noise and quantization floor of 50 micrometers. These results hold even in the presence of realistic parametric uncertainty in actual spacecraft inertia, center-of-mass offset, and first flexibility modes. Thrust profile knowledge is shown to be a dominant sensitivity for G-Sample, entering in a nearly one-to-one relationship with the final mass estimation error. This means thrust profiles should be well characterized with onboard accelerometers prior to sample collection. An overall sample-mass estimation error budget has been developed to approximate the effect of model uncertainty, sensor noise, data rate, and thrust profile error on the expected estimate of collected sample mass.

Carson, John↗
NASA NTRS2010

Characterization of Cloud Water-Content Distribution

The development of realistic cloud parameterizations for climate models requires accurate characterizations of subgrid distributions of thermodynamic variables. To this end, a software tool was developed to characterize cloud water-content distributions in climate-model sub-grid scales. This software characterizes distributions of cloud water content with respect to cloud phase, cloud type, precipitation occurrence, and geo-location using CloudSat radar measurements. It uses a statistical method called maximum likelihood estimation to estimate the probability density function of the cloud water content.

Lee, Seungwon↗
NASA NTRS2005

Program for Weibull Analysis of Fatigue Data

A Fortran computer program has been written for performing statistical analyses of fatigue-test data that are assumed to be adequately represented by a two-parameter Weibull distribution. This program calculates the following: (1) Maximum-likelihood estimates of the Weibull distribution; (2) Data for contour plots of relative likelihood for two parameters; (3) Data for contour plots of joint confidence regions; (4) Data for the profile likelihood of the Weibull-distribution parameters; (5) Data for the profile likelihood of any percentile of the distribution; and (6) Likelihood-based confidence intervals for parameters and/or percentiles of the distribution. The program can account for tests that are suspended without failure (the statistical term for such suspension of tests is "censoring"). The analytical approach followed in this program for the software is valid for type-I censoring, which is the removal of unfailed units at pre-specified times. Confidence regions and intervals are calculated by use of the likelihood-ratio method.

Krantz, Timothy L.↗
NASA NTRS2006

Small Body GN and C Research Report: G-SAMPLE - An In-Flight Dynamical Method for Identifying Sample Mass [External Release Version]

G-SAMPLE is an in-flight dynamical method for use by sample collection missions to identify the presence and quantity of collected sample material. The G-SAMPLE method implements a maximum-likelihood estimator to identify the collected sample mass, based on onboard force sensor measurements, thruster firings, and a dynamics model of the spacecraft. With G-SAMPLE, sample mass identification becomes a computation rather than an extra hardware requirement; the added cost of cameras or other sensors for sample mass detection is avoided. Realistic simulation examples are provided for a spacecraft configuration with a sample collection device mounted on the end of an extended boom. In one representative example, a 1000 gram sample mass is estimated to within 110 grams (95% confidence) under realistic assumptions of thruster profile error, spacecraft parameter uncertainty, and sensor noise. For convenience to future mission design, an overall sample-mass estimation error budget is developed to approximate the effect of model uncertainty, sensor noise, data rate, and thrust profile error on the expected estimate of collected sample mass.

in-flight sample-mass identification↗
NASA NTRS2012

Identification of Time-Varying Pilot Control Behavior in Multi-Axis Control Tasks

Recent developments in fly-by-wire control architectures for rotorcraft have introduced new interest in the identification of time-varying pilot control behavior in multi-axis control tasks. In this paper a maximum likelihood estimation method is used to estimate the parameters of a pilot model with time-dependent sigmoid functions to characterize time-varying human control behavior. An experiment was performed by 9 general aviation pilots who had to perform a simultaneous roll and pitch control task with time-varying aircraft dynamics. In 8 different conditions, the axis containing the time-varying dynamics and the growth factor of the dynamics were varied, allowing for an analysis of the performance of the estimation method when estimating time-dependent parameter functions. In addition, a detailed analysis of pilots adaptation to the time-varying aircraft dynamics in both the roll and pitch axes could be performed. Pilot control behavior in both axes was significantly affected by the time-varying aircraft dynamics in roll and pitch, and by the growth factor. The main effect was found in the axis that contained the time-varying dynamics. However, pilot control behavior also changed over time in the axis not containing the time-varying aircraft dynamics. This indicates that some cross coupling exists in the perception and control processes between the roll and pitch axes.

Zaal, Peter M. T.↗
NASA NTRS2014

How Well Does the Latest Anthropomorphic Test Device Mimic Human Impact Responses?

One of the goals of the NASA Occupant Protection Group is to understand the human tolerance to dynamic loading. This knowledge has to come through indirect approaches such as existing human response databases, anthropometric test devices (ATD), animal testing, post‐mortem human subjects, and models. This study investigated the biofidelity of the National Highway Traffic Safety Administration's ATD named the THOR (test device for human occupant restraint). If THOR responds comparably to humans, then it could potentially be used as a human surrogate to help validate space vehicle requirements for occupant protection. The THOR responses to frontal and spinal impacts (ranging from 8 to 12 G with rise times of 40, 70, and 100 ms) were measured and compared to human volunteer responses (95 trials in frontal and 58 in spinal) previously collected by the U. S. Air Force on the same horizontal impact accelerator. The impact acceleration profiles tested are within the expected range of multi‐purpose crew vehicle (MPCV) landing dynamics. A correlation score was calculated for each THOR to human comparison using CORA (CORrelation and Analysis) software. A two‐parameter beta distribution model fit was obtained for each dependent variable using maximum likelihood estimation. For frontal impacts, the THOR head x‐acceleration peak response correlated with the human response at 8 and 10‐G 100 ms but not 10‐G 70 ms. The phase lagged the human response. Head z‐acceleration was not correlated. Chest x‐acceleration was in phase, had a higher peak response, and was well correlated with lighter subjects (Cora = 0.8 for 46 kg vs. Cora = 0.4 for 126 kg). Head x‐displacement had a leading phase. Several subjects responded with the same peak displacement but the mean of the group was lower. The shoulder x‐displacement was in phase but had higher peaks than the human response. For spinal impacts, the THOR head x‐acceleration was not well correlated. Head and chest z‐acceleration was in phase but had a higher peak response. Chest z‐acceleration was highly correlated with heavier subjects at lower G pulses (Cora = 0.86 for 125 kg at 8 G). The human response was variable in shoulder z‐displacement but the THOR was in phase and was comparable to the mean peak response. Head xand z‐displacement was in phase but had higher peaks. Seat pan forces were well correlated, were in phase, but had a larger peak response than most subjects. The THOR does not respond to frontal and spinal impacts exactly the same way that a human does. Some responses are well matched and others are not. Understanding the strengths and weaknesses of this ATD is an important first step in determining its usefulness in occupant protection at NASA

Newby, N.↗
NASA NTRS2014

How Well Does the Latest Anthropomorphic Test Device Mimic Human Impact Responses?

One of the goals of the NASA Occupant Protection Group is to understand the human tolerance to dynamic loading. This knowledge has to come through indirect approaches such as existing human response databases, anthropometric test devices (ATD), animal testing, post-­‐mortem human subjects, and models. This study investigated the biofidelity of the National Highway Traffic Safety Administration's ATD named the THOR (test device for human occupant restraint). If THOR responds comparably to humans, then it could potentially be used as a human surrogate to help validate space vehicle requirements for occupant protection. The THOR responses to frontal and spinal impacts (ranging from 8 to 12 G with rise times of 40, 70, and 100 ms) were measured and compared to human volunteer responses (95 trials in frontal and 58 in spinal) previously collected by the U. S. Air Force on the same horizontal impact accelerator. The impact acceleration profiles tested are within the expected range of multi-­‐purpose crew vehicle (MPCV) landing dynamics. A correlation score was calculated for each THOR to human comparison using CORA (CORrelation and Analysis) software. A two-­‐parameter beta distribution model fit was obtained for each dependent variable using maximum likelihood estimation. For frontal impacts, the THOR head x-­‐acceleration peak response correlated with the human response at 8 and 10-­‐G 100 ms but not 10-­‐G 70 ms. The phase lagged the human response. Head z-­‐acceleration was not correlated. Chest x-­‐acceleration was in phase, had a higher peak response, and was well correlated with lighter subjects (Cora = 0.8 for 46 kg vs. Cora = 0.4 for 126 kg). Head x-­‐displacement had a leading phase. Several subjects responded with the same peak displacement but the mean of the group was lower. The shoulder x-­‐displacement was in phase but had higher peaks than the human response. For spinal impacts, the THOR head x-­‐acceleration was not well correlated. Head and chest z-­‐acceleration was in phase but had a higher peak response. Chest z-­‐acceleration was highly correlated with heavier subjects at lower G pulses (Cora = 0.86 for 125 kg at 8 G). The human response was variable in shoulder z-­‐displacement but the THOR was in phase and was comparable to the mean peak response. Head x-­‐ and z-­‐displacement was in phase but had higher peaks. Seat pan forces were well correlated, were in phase, but had a larger peak response than most subjects. The THOR does not respond to frontal and spinal impacts exactly the same way that a human does. Some responses are well matched and others are not. Understanding the strengths and weaknesses of this ATD is an important first step in determining its usefulness in occupant protection at NASA

Newby, Nate↗