Search NASA⌕ Search

SEARCH · Search NASA

Results for “conditional random fields”

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.

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

At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

A High Performance Computing Approach to Tree Cover Delineation in 1-m NAIP Imagery Using a Probabilistic Learning Framework

Tree cover delineation is a useful instrument in deriving Above Ground Biomass (AGB) density estimates from Very High Resolution (VHR) airborne imagery data. Numerous algorithms have been designed to address this problem, but most of them do not scale to these datasets, which are of the order of terabytes. In this paper, we present a semi-automated probabilistic framework for the segmentation and classification of 1-m National Agriculture Imagery Program (NAIP) for tree-cover delineation for the whole of Continental United States, using a High Performance Computing Architecture. Classification is performed using a multi-layer Feedforward Backpropagation Neural Network and segmentation is performed using a Statistical Region Merging algorithm. The results from the classification and segmentation algorithms are then consolidated into a structured prediction framework using a discriminative undirected probabilistic graphical model based on Conditional Random Field, which helps in capturing the higher order contextual dependencies between neighboring pixels. Once the final probability maps are generated, the framework is updated and re-trained by relabeling misclassified image patches. This leads to a significant improvement in the true positive rates and reduction in false positive rates. The tree cover maps were generated for the whole state of California, spanning a total of 11,095 NAIP tiles covering a total geographical area of 163,696 sq. miles. The framework produced true positive rates of around 88% for fragmented forests and 74% for urban tree cover areas, with false positive rates lower than 2% for both landscapes. Comparative studies with the National Land Cover Data (NLCD) algorithm and the LiDAR canopy height model (CHM) showed the effectiveness of our framework for generating accurate high-resolution tree-cover maps.

Segments↗
NASA NTRS2016

DeepSAT: A Deep Learning Approach to Tree-Cover Delineation in 1-m NAIP Imagery for the Continental United States

High resolution tree cover classification maps are needed to increase the accuracy of current land ecosystem and climate model outputs. Limited studies are in place that demonstrates the state-of-the-art in deriving very high resolution (VHR) tree cover products. In addition, most methods heavily rely on commercial softwares that are difficult to scale given the region of study (e.g. continents to globe). Complexities in present approaches relate to (a) scalability of the algorithm, (b) large image data processing (compute and memory intensive), (c) computational cost, (d) massively parallel architecture, and (e) machine learning automation. In addition, VHR satellite datasets are of the order of terabytes and features extracted from these datasets are of the order of petabytes. In our present study, we have acquired the National Agriculture Imagery Program (NAIP) dataset for the Continental United States at a spatial resolution of 1-m. This data comes as image tiles (a total of quarter million image scenes with ~60 million pixels) and has a total size of ~65 terabytes for a single acquisition. Features extracted from the entire dataset would amount to ~8-10 petabytes. In our proposed approach, we have implemented a novel semi-automated machine learning algorithm rooted on the principles of "deep learning" to delineate the percentage of tree cover. Using the NASA Earth Exchange (NEX) initiative, we have developed an end-to-end architecture by integrating a segmentation module based on Statistical Region Merging, a classification algorithm using Deep Belief Network and a structured prediction algorithm using Conditional Random Fields to integrate the results from the segmentation and classification modules to create per-pixel class labels. The training process is scaled up using the power of GPUs and the prediction is scaled to quarter million NAIP tiles spanning the whole of Continental United States using the NEX HPC supercomputing cluster. An initial pilot over the state of California spanning a total of 11,095 NAIP tiles covering a total geographical area of 163,696 sq. miles has produced true positive rates of around 88 percent for fragmented forests and 74 percent for urban tree cover areas, with false positive rates lower than 2 percent for both landscapes.

Imagery↗
NASA NTRS2017

Marve: Measurement Context Extraction from Text

We propose Marve, a system for extracting measurement values, units, and related words from natural language text. Marve uses conditional random fields (CRF) to identify measurement values and units, followed by a rule-based system to find related entities, descriptors and modifiers within a sentence. Sentence tokens are represented by an undirected graphical model, and rules are based on part-of-speech and word dependency patterns connecting values and units to contextual words. Marve is unique in its focus on measurement context and early experimentation demonstrates Marve’s ability to generate high-precision extractions with strong recall. We also discuss Marve’s role in justifying NASA JPL’s proposed HyspIRI mission, a hyper spectral infrared imaging satellite that will study the world’s ecosystems. In general, our work with HyspIRI demonstrates the value of semantic measurement extractions in characterizing quantitative discussion contained in large corpuses of natural language text. These extractions accelerate broad-cross cu ing literature surveys and expose researchers and scientists new algorithmic approaches and experimental nuances. They also facilitate identification of scientific opportunities enabled by HyspIRI leading to more informed scientific investment and research.

Mattmann, Chris A.↗
NASA NTRSDate not supplied

Characterizing Spatiotemporal Uncertainty in Interpolated Meteorological Data

Interpolated meteorological data invariably contain errors. These errors have structure in time and space, particularly autocorrelation, which can cause the effects of errors to compound when model outputs are aggregated temporally or spatially. One way to account for this uncertainty is with a probabilistic model from which samples can be drawn that are coherent with respect to underlying spatial and temporal covariance structure. This work describes a probabilistic method for spatial interpolation of point-wise meteorological time series. Observational data from weather stations are generally sparse in space and dense in time (but sometimes missing). The method works by projecting time series onto orthogonal basis vectors and spatially interpolating each resulting component independently. Under suitable assumptions, and data transformations to better satisfy those assumptions, Gaussian process regression provides a complete description of the joint predictive distribution over a Gaussian random field. Spatiotemporally coherent realizations are generated as the sum of conditional (spatial) simulations of each orthogonal (temporal) component. Data-derived and generic orthogonal bases are considered. In addition to spatial interpolation, imputation of missing observational data is examined. The method is applied using near-surface air temperature over the Western United States and validated by comparing theoretical versus actual coverage of predictive distributions and analyzing the degree to which spatial and temporal covariance structure is reproduced. Computational considerations, relating to conditional simulation of random fields, are also addressed.

Conor T Doherty↗
NASA NTRS1976

One-dimensional numerical study of charged particle trajectories in turbulent electrostatic wave fields

The paper describes a numerical simulation of electron trajectories in weak random electric fields under conditions that are approximately true for Langmuir waves whose wavelength is much longer than the Debye length. Two types of trajectory calculations were made: (1) the initial particle velocity was made equal to the mean phase velocity of the waves, or (2) it was equal to 0.7419 times the mean velocity of the waves, so that the initial velocity differed substantially from all phase velocities of the wave spectrum. When the autocorrelation time is much greater than the trapping time, the particle motion can change virtually instantaneously from one of three states - high-velocity, low-velocity, or trapped state - to another. The probability of instantaneous transition from a high- or low-velocity state becomes small when the difference between the particle velocity and the mean phase velocity of the waves becomes high in comparison to the trapping velocity. Diffusive motion becomes negligible under these conditions also.

Graham, K. N.↗
NASA NTRS1987

Influence of visual and motion cues on manual lateral stabilization

The ability of humans to detect and control their own lateral acceleration was measured by means of a closed-loop nulling task. Wide-field moving visual cues enhanced the operator's performance in nulling self-motion, especially at lower frequencies. Even visual cues, fixed relative to the operator, resulted in performance improvement relative to self-motion nulling in the dark. Describing function (frequency response) data was obtained for random acceleration conditions consisting of the visual field fixed relative to the laboratory, fixed relative to the subject, moving at constant velocity relative to the subject, and in the dark. The describing function data was corrected for the dynamics of operator manual control strategy. The resulting frequency responses were used to develop a linear model of self-motion detection which required a lead term of 1.5 rad/s to be added to the existing model in order to match the higher sensitivity at higher frequencies.

Huang, J.-K.↗
NASA NTRS1974

Pitch-angle scattering of charged particles in a random magnetic field

A calculation is presented in terms of the pitch angle that determines the conditions under which a Fokker-Planck equation gives a reasonable approximation of the pitch-angle scattering of low rigidity particles to first order in a random magnetic field. The formulation shows that the correlation scale of the fluctuation of the magnetic field about its mean does not enter directly into the approximation. The calculation is carried out for transverse magnetic fluctuations for which the magnetic field magnitude is constant to the order considered.

Jokipii, J. R.↗
NASA NTRS1993

Wind-forced variability of the deep eastern north Pacific: Observations of seafloor pressure and abyssal currents

Data from an array of bottom pressure gauges and a string of current meters in the vicinity of 47 deg N, 139 deg W, are used to examine the deep-ocean variability forced by ocean surface wind stress curl from August 1987 to June 1988. Bottom geostrophic currents are computed from the pressure gauge array, and these correspond well to the long-period directly measured currents at 3000 m. The supratidal-period bottom pressure variations are coherent at 95% confidence with the wind stress curl in period bands of 3-4 days and 15-60 days but removed in distances of 400 and 700 km to the northwest and the southeast, respectively. A linear, two-layer hydrodynamic model is used to examine the theoretical forcing produced by random-phased surface wind fields for the conditions of the eastern north Pacific and the 15- to 60-day-period observed response is reproduced credibly. To model 3- to 15-day variations, more realistic models are required.

Niller, P. P.↗
NASA NTRS1975

Simulation of the velocity diffusion of charged particles in turbulent magnetic fields

Numerical simulation experiments have been performed in order to study the velocity diffusion of charged particles in a static turbulent magnetic field. By following orbits of particles moving in a large ensemble of random magnetic field realizations with suitably-chosen statistical properties, a pitch-angle diffusion coefficient is derived. Results are presented for a variety of particle rigidities and r.m.s. random field strengths and a power spectrum typical of conditions found in interplanetary space.

Kaiser, T. B.↗
NASA NTRS1995

Spatial Factors in the Integration of Speed Information

We reported that, for a 21FC task with multiple Gabor patches in each interval, thresholds for speed discrimination decreased with the number of patches, while simply increasing the area of a single patch produced no such effect. This result could be explained by multiple patches reducing spatial uncertainty. However, the fact that thresholds decrease with number even when the patches are in fixed positions argues against this explanation. We therefore performed additional experiments to explore the lack of an area effect. Three observers did a 21FC speed discrimination task with 6 Gabor patches in each interval, and were asked to pick the interval in which the gratings moved faster. The 50% contrast patches were placed on a circle at 4 deg. eccentricity, either equally spaced and maximally separated (hexagonal array), or closely-spaced, in consecutive positions (string of pearls). For the string-of-pearls condition, the grating phases were either random, or consistent with a full-field grating viewed through multiple Gaussian windows. When grating phases were random, the thresholds for the hexagonal and string-of-pearls layouts were indistinguishable. For the string-of-pearls layout, thresholds in the consistent-phase condition were higher by 15 +/- 6% than in the random-phase condition. (Thresholds increased by 57 +/- 7% in going from 6 patches to a single patch of equivalent area.). For random-phase patches, the lower thresholds for 6 patches does not depend on a specific spacing or spatial layout. Multiple, closely-spaced, consistent-phase patches that can be interpreted as a single grating, result in thresholds closer to that produced by a single patch. Together, our results suggest that object segmentation may play a role in the integration of speed information.

Verghese, P.↗
NASA NTRS2019

Electromagnetic Scattering by Discrete Random Media. II: The Coherent Field

The computation of the coherent field in the case of a plane electromagnetic wave obliquely incident on a discrete random layer with non-scattering boundaries is addressed. For dense media, the analysis is based on a special-form solution for the conditional configuration-averaged exciting field coefficients, and is restricted to the computation of the so-called zeroth-order fields without a special treatment of the boundary regions. In this setting, we calculate the coherent fields reflected and transmitted by the layer, and the coherent field inside the layer. We found that these fields are analytically equivalent to plane electromagnetic waves, and investigated the fulfillment of the boundary conditions for the electric fields at the layer interfaces. The results are then particularized to the cases of normal incidence and a semi-infinite discrete random medium. For sparsely distributed particles, we present a self-consistent derivation of the coherent field and discuss the Twersky and Foldy approximations.

Adrian Doicu↗
NASA NTRS1995

Test particle study of minor ions in solar wind turbulence

We perform a parameter study of the temporal evolution of a test particle distribution function in MHD turbulence. The turbulent fields are calculated using a pseudo-spectral method and periodic boundary conditions on a regular grid of 180(exp 3) points, appropriate for incompressible, homogeneous and isotropic turbulence. Initially, the kinetic and the magnetic energy are equal on the average. Both, deterministic and random initial conditions are used, in the former case with zeros of the magnetic field located at grid points, in the latter case located by interpolation between grid points. The evolution of the minor ion distribution function is studied in detail as these turbulent fields evolve, developing strong current and vorticity sheets. Using the full collisionless equation of motion for the test particles, the efficiency of nonlinear interactions can be studied. The results are compared to theoretical predictions and are then discussed in connection with the observations of the dynamical properties of solar wind minor ions derived from in situ observations.

Zurbuchen, Th.↗
NASA NTRS2019

Electromagnetic Scattering by Discrete Random Media. I: The Dispersion Equation and the Configuration-Averaged Exciting Field

We consider the scattering of a plane electromagnetic wave obliquely incident on a plane-parallel layer of discrete random medium with non-scattering boundaries. We solve the Lax integral equation for the conditional configuration-averaged exciting field coefficients by assuming a special-form solution, that is, by representing the conditional configuration-averaged exciting field coefficients as a linear combination of the coefficients corresponding to an up-going and a down-going wave. This solution representation is supposed to be valid within the whole domain occupied by the particles, even in the close proximity of the boundaries. By balancing the waves with different propagation directions and wavenumbers we derive two homogeneous systems of equations corresponding to the generalized Lorenz–Lorentz law and two inhomogeneous systems of equations corresponding to the generalized Ewald–Oseen extinction theorem. It is shown that (i) the two homogeneous systems of equations of the generalized Lorenz–Lorentz law reduce to a single homogeneous system of equations corresponding to a semi-infinite discrete random medium at normal incidence; (ii) the dispersion equation is direction and polarization independent; and (iii) the two inhomogeneous systems of equations of the generalized Ewald–Oseen extinction theorem can be reduced to two scalar equations by means of the addition theorem for vector spherical harmonics. It is also shown that the same dispersion equation can be obtained without assuming a special-form solution representation in the proximity of the boundaries.

Adrian Doicu↗
NASA NTRS1993

The effect of tidal fields on the shapes and kinematics of dark halos

We have carried out a series of N-body simulations to investigate the effect of tidal shear on the structure and kinematics of dark halos. We simulate the collapse of density perturbations using a tree code as described in Dubinski & Carlberg (1991). Density peaks are selected from a random realization of a CDM density field and used as the initial conditions for N-body simulations. We use an experimental approach to examine the effects of tidal shear on collapse. The cosmological tidal field is treated as an external time dependent potential whose strength and orientation can be varied freely. We examine the effects of the tidal field with two experiments. In the first experiment, we simulate a sample of 14 dark halos from the collapse of density peaks in the presence of a 1(sigma) tidal field. In the second experiment, we use the same initial conditions though the tidal field is turned off allowing an experimental control for comparison to highlight the influence of tidal shear on the development of the structure and kinematics of the dark halos.

Dubinski, John↗
NASA NTRS2020

Designing an Optimal Ensemble Strategy for GMAO S2S Forecast System

GMAO Sub/Seasonal prediction system (S2S) is being readied for a major upgrade to GEOS-S2S Version 3. An important factor in successful extended range forecast is the definition of an ensemble For initialization of the ensemble we propose a combination of lagged and burst initial conditions. We plan to run a relatively large ensemble of 40 members for sub-seasonal forecast (up to 3 months), at which point we sub-sample the ensemble, and continue the forecast with 10 members (up to 12 months). Here we present the results of the extensive testing of various ways to generate the perturbations to the initial conditions and the validation of the stratified sampling strategy we chose.To generate perturbations for the burst ensemble members we used scaled differences of pairs of analysis states separated by 1-10 days, randomly chosen from a corresponding season. We considered perturbing separately only the atmospheric fields or only the ocean or both of the forecast initial conditions. Considering varying separation times between the analysis states, we were able to produce perturbations sampling various modes of variability. Focusing on the ENSO SST indices, we found that all types of perturbations are important for the ensemble spread.Our ensemble size for sub-seasonal forecasts was determined as to maximize the skill of predicting some of the leading modes of boreal winter atmospheric modes, NAO, PNA and AO. It is not feasible to run equally large ensemble for seasonal forecasts. Using a stratified sampling procedure we can identify the emerging directions of error growth. By comparing the stratified ensemble with randomly sampled ensemble of the same size, we were able to show that the former better estimates the mean of the original large ensemble.

Borovikov, Anna↗
NASA NTRS1998

Estimation of 3-D Cloud Effects on TOMS Satellite Retrieval of Surface UV Irradiance

To improve surface UV irradiance retrieval from the Total Ozone Mapping Spectrometer (TOMS) we simulate errors of the TOMS cloud correction algorithm for summertime broken cloud conditions. Cloud scenes (50 km by 50 km) are modeled by a normal random (Gaussian) field with a fixed lower boundary and conservative scattering. The model relates stochastic field characteristics with the cloud amount, mean cloud diameter and aspect ratio. Clouds are embedded into Rayleigh atmosphere with standard ozone profile. Radiative transfer calculations of the radiance at the top of the atmosphere and irradiance at the surface were performed using 3-D Monte Carlo (MC) code. The results are averaged over the satellite field of view on the surface (50 km by 50 km) and compared with TOMS predicted surface irradiance for the same scene reflectance. The TOMS algorithm assumes horizontally homogeneous Cl-type cloud between 3 km and 5.5 km. The effective optical depth is determined by fitting observed (MC) radiance at 380 nm. Having the same radiance at the satellite the homogeneous and broken cloud models predict different average irradiances at the surface. This is due to the differences in Bidirectional Reflection Distribution Function (BRDF) for homogeneous and broken cloud scenes with the same hemispherical albedo. For typical TOMS observational geometry at mid-latitudes the simulated single pixels errors may be as large as +/- 20%. Qualitatively these errors are due to the dominance of the non-horizontal cloud surfaces, which are not accounted for in the homogeneous cloud model. However, due to high variability of the real cloud shapes and types it is unclear how these single pixel errors would affect TOMS time-integrated UV exposure over extended periods (weeks to months) for different regions.

Krotkov, Nickolay A.↗
NASA NTRS2014

Progress of a Cross-Correlation Based Optical Strain Measurement Technique for Detecting Radial Growth on a Rotating Disk

The Aeronautical Sciences Project under NASA's Fundamental Aeronautics Program is interested in the development of novel measurement technologies, such as optical surface measurements for the in situ health monitoring of critical constituents of the internal flow path. In situ health monitoring has the potential to detect flaws, i.e. cracks in key components, such as engine turbine disks, before the flaws lead to catastrophic failure. The present study, aims to further validate and develop an optical strain measurement technique to measure the radial growth and strain field of an already cracked disk, mimicking the geometry of a sub-scale turbine engine disk, under loaded conditions in the NASA Glenn Research Center's High Precision Rotordynamics Laboratory. The technique offers potential fault detection by imaging an applied high-contrast random speckle pattern under unloaded and loaded conditions with a CCD camera. Spinning the cracked disk at high speeds (loaded conditions) induces an external load, resulting in a radial growth of the disk of approximately 50.0-μm in the flawed region and hence, a localized strain field. When imaging the cracked disk under static conditions, the disk will be undistorted; however, during rotation the cracked region will grow radially, thus causing the applied particle pattern to be 'shifted'. The resulting particle displacements between the two images is measured using the two-dimensional cross-correlation algorithms implemented in standard Particle Image Velocimetry (PIV) software to track the disk growth, which facilitates calculation of the localized strain field. A random particle distribution is adhered onto the surface of the cracked disk and two bench top experiments are carried out to evaluate the technique's ability to measure the induced particle displacements. The disk is shifted manually using a translation stage equipped with a fine micrometer and a hotplate is used to induce thermal growth of the disk, causing the particles to become shifted. For both experiments, reference and test images are acquired before and after the induced shifts, respectively, and then processed using PIV software. The controlled manual translation of the disk resulted in detection of the particle displacements accurate to ~1.75% of full scale and the thermal expansion experiment resulted in successful detection of the disk's thermal growth as compared to the calculated thermal expansion results. After validation of the technique through the induced shift experiments, the technique is implemented in the Rotordynamics Lab for preliminary assessment in a simulated engine environment. The discussion of the findings and plans for future work to improve upon the results are addressed in the paper.

rotordynamics↗
NASA NTRS1990

Stabilization functions of unforced cumulus clouds - Their nature and components

The nature and the spatial distribution of cumuli within an unforced cumulus cloud field are investigated. The thermodynamic effects of convection are quantified as functions of changes of convective available potential energy (CAPE) induced by the convective overturning, and the time rate of change of CAPE is parameterized in terms of a kernel of influence or stabilization function. A three-dimensional cloud model is used to infer and quantify stabilization functions by performing single-cloud experiments. On the basis of the results obtained, a new hypothesis with respect to the spatial distribution of cumuli is postulated, which states that, under completely homogeneous external conditions and assuming a spatially random distribution of cloud-triggering mechanisms, the spatial distribution of cumuli in the resulting cloud field must be regular, as opposed to either random or clustered, because cumulus clouds tend to reduce the available energy for convection, thereby inhibiting further convection nearby.

Ramirez, Jorge A.↗