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At least 343 records · Page 19

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NASA NTRSDate not supplied

A Principal-Component-Based Radiative Transfer Model (PCRTM) for Hyperspectral Shortwave and Longwave Satellite Sensors and Its Applications

The radiative transfer model (RTM) or forward model is an essential component in satellite remote sensing. For modern hyperspectral remote sensors, fast and accurate RTMs are needed due to a large number of spectral dimensions and high spatial resolutions. We will describe a Principal Component-based radiative transfer model (PCRTM) which can simulate the top-of-atmosphere (TOA) radiance or reflectance spectra 250 nm to 2000 micrometers quickly and accurately. The PCRTM has been demonstrated to be extremely accurate, compared to the line-by-line RTM benchmarks, and the former is several orders more computationally efficient than the latter. We will demonstrate how the PCRTM and the associated inversion algorithms are used to infer atmospheric temperature, moisture, and trace gas profiles, as well as cloud and surface properties from hyperspectral IR sounders such as Atomspheric Infrared Souder (AIRS) and Cross-track Infrared Sounder (CrIS). High-quality climate records for a 20-year duration have been derived from these IR hyperspectral data. Finally, we will show some examples of using PCRTM to retrieve cloud properties from Earth Surface Mineral Dust Source Investigation (EMIT) and its applicability of PCRTM to future missions such as the CLARREO (Climate Absolute Radiance and Refractivity Observatory) Pathfinder (CPF) CPF and the Surface Biology and Geology (SBG).

Xu Liu↗

NASA NTRS2015

Desensitized Optimal Filtering and Sensor Fusion Toolkit

Analytical Mechanics Associates, Inc., has developed a software toolkit that filters and processes navigational data from multiple sensor sources. A key component of the toolkit is a trajectory optimization technique that reduces the sensitivity of Kalman filters with respect to model parameter uncertainties. The sensor fusion toolkit also integrates recent advances in adaptive Kalman and sigma-point filters for non-Gaussian problems with error statistics. This Phase II effort provides new filtering and sensor fusion techniques in a convenient package that can be used as a stand-alone application for ground support and/or onboard use. Its modular architecture enables ready integration with existing tools. A suite of sensor models and noise distribution as well as Monte Carlo analysis capability are included to enable statistical performance evaluations.

Karlgaard, Christopher D.↗

NASA NTRS1996

Alpha-Particle Gas-Pressure Sensor

An approximate model was developed to establish design curves for the saturation region and a more complete model developed to characterize the current-voltage curves for an alpha-particle pressure sensor. A simple two-parameter current-voltage expression was developed to describe the dependence of the ion current on pressure. The parameters are the saturation-current pressure coefficient and mu/D, the ion mobility/diffusion coefficient. The sensor is useful in the pressure range between 0.1 and 1000 mb using a 1 - mu Ci(241) Am source. Experimental results, taken between 1 and up to 200 mb, show the sensor operates with an anode voltage of 5 V and a sensitivity of 20 fA/mb in nitrogen.

Buehler, M. C.↗

NASA NTRS2012

Particle Filtering for Model-Based Anomaly Detection in Sensor Networks

A novel technique has been developed for anomaly detection of rocket engine test stand (RETS) data. The objective was to develop a system that postprocesses a csv file containing the sensor readings and activities (time-series) from a rocket engine test, and detects any anomalies that might have occurred during the test. The output consists of the names of the sensors that show anomalous behavior, and the start and end time of each anomaly. In order to reduce the involvement of domain experts significantly, several data-driven approaches have been proposed where models are automatically acquired from the data, thus bypassing the cost and effort of building system models. Many supervised learning methods can efficiently learn operational and fault models, given large amounts of both nominal and fault data. However, for domains such as RETS data, the amount of anomalous data that is actually available is relatively small, making most supervised learning methods rather ineffective, and in general met with limited success in anomaly detection. The fundamental problem with existing approaches is that they assume that the data are iid, i.e., independent and identically distributed, which is violated in typical RETS data. None of these techniques naturally exploit the temporal information inherent in time series data from the sensor networks. There are correlations among the sensor readings, not only at the same time, but also across time. However, these approaches have not explicitly identified and exploited such correlations. Given these limitations of model-free methods, there has been renewed interest in model-based methods, specifically graphical methods that explicitly reason temporally. The Gaussian Mixture Model (GMM) in a Linear Dynamic System approach assumes that the multi-dimensional test data is a mixture of multi-variate Gaussians, and fits a given number of Gaussian clusters with the help of the wellknown Expectation Maximization (EM) algorithm. The parameters thus learned are used for calculating the joint distribution of the observations. However, this GMM assumption is essentially an approximation and signals the potential viability of non-parametric density estimators. This is the key idea underlying the new approach.

Solano, Wanda↗

NASA NTRS2014

Robust Modal Filtering and Control of the X-56A Model with Simulated Fiber Optic Sensor Failures

The X-56A aircraft is a remotely-piloted aircraft with flutter modes intentionally designed into the flight envelope. The X-56A program must demonstrate flight control while suppressing all unstable modes. A previous X-56A model study demonstrated a distributed-sensing-based active shape and active flutter suppression controller. The controller relies on an estimator which is sensitive to bias. This estimator is improved herein, and a real-time robust estimator is derived and demonstrated on 1530 fiber optic sensors. It is shown in simulation that the estimator can simultaneously reject 230 worst-case fiber optic sensor failures automatically. These sensor failures include locations with high leverage (or importance). To reduce the impact of leverage outliers, concentration based on a Mahalanobis trim criterion is introduced. A redescending M-estimator with Tukey bisquare weights is used to improve location and dispersion estimates within each concentration step in the presence of asymmetry (or leverage). A dynamic simulation is used to compare the concentrated robust estimator to a state-of-the-art real-time robust multivariate estimator. The estimators support a previously-derived mu-optimal shape controller. It is found that during the failure scenario, the concentrated modal estimator keeps the system stable.

Modal Filtering↗

NASA NTRS2016

Robust Modal Filtering and Control of the X-56A Model with Simulated Fiber Optic Sensor Failures

Modal Filtering↗

DOE OSTI2022

Physics-based modeling and information-theoretic sensor and settings selection for tool wear detection in precision machining

Precision machining of metals is an energy intensive process with applications and impacts across the manufacturing industry. The energy efficiency, product yield, and maintenance of the precision machine require a digital twin that can assist with prognostics and health management. In this report a physics-based model is developed and validated against face milling data, and then used for the timely and precise inference of machining faults that cannot be measured directly. Computer numerical control (CNC) measurements of power and force are used through this physics-based machining model to predict deviations of the outputs of power consumption and cutting forces during normal operation. A model-based fault detection and isolation methodology is applied to determine the optimal (traditional and available) sensor suite and the test settings (admissible input values) that improve the inference of tool wear in face milling. The optimal sensor suite and input test settings are obtained by solving a mixed integer non-linear program that optimizes information-theoretic metrics relevant to the detection and isolation of tool wear from steady-state or transient machining measurements. Dynamic time warping and k—NN classification are then used to validate the robustness of the optimal design for fault detection test design, including the optimal sensor suite.

42 ENGINEERING↗

NASA NTRS2016

Full-Field Reconstruction of Structural Deformations and Loads from Measured Strain Data on a Wing Using the Inverse Finite Element Method

A study was undertaken to investigate the measurement of wing deformation and internal loads using measured strain data. Future aerospace vehicle research depends on the ability to accurately measure the deformation and internal loads during ground testing and in flight. The approach uses the inverse Finite Element Method (iFEM). The iFEM is a robust, computationally efficient method that is well suited for real-time measurement of real-time structural deformation and loads. The method has been validated in previous work, but has yet to be applied to a large-scale test article. This work is in preparation for an upcoming loads test of a half-span test wing in the Flight Loads Laboratory at the National Aeronautics and Space Administration Armstrong Flight Research Center (Edwards, California). The method has been implemented into an efficient MATLAB® (The MathWorks, Inc., Natick, Massachusetts) code for testing different sensor configurations. This report discusses formulation and implementation along with the preliminary results from a representative aerospace structure. The end goal is to investigate the modeling and sensor placement approach so that the best practices can be applied to future aerospace projects.

Finite element↗

DOE OSTI2015

Social Sensor Analytics: Making Sense of Network Models in Social Media

Social networks can be thought of as noisy sensor networks mapping real world information to the web. Owing to the extensive body of literature in sensor network analysis, this work sought to apply several novel and traditional methods in sensor network analysis for the purposes of efficiently interrogating social media data streams from raw data. We carefully revisit our definition of a social media signal from previous work both in terms of time-varying features within the data and the networked nature of the medium. Further, we detail our analysis of global patterns in Twitter over the months of November 2013 and June 2014, detect and categorize events, and illustrate how these analyses can be used to inform graph-based models of Twitter, namely using a recent network influence model called PhySense: similar to PageRank but tuned to behavioral analysis by leveraging a sociologically inspired probabilistic model. We ultimately identify forms of information dissemination via analysis of time series and dynamic graph spectra and corroborate these findings through manual investigation of the data as a requisite step in modeling the diffusion process with PhySense. We hope to sufficiently characterize global behavior in a medium such as Twitter as a means of learning global model parameters one may use to predict or simulate behavior on a large scale. We have made our time series and dynamic graph analytical code available via a GitHub repository https://github.com/cpatdowling/salsa and our data are available upon request.

event detection↗

DOE OSTI2014

An in-depth noise model for giant magnetoresistance current sensors for circuit design and complementary metal–oxide–semiconductor integration

Full instrumentation bridges based on spin valve of giant magnetoresistance and magnetic tunnel junction devices have been microfabricated and experimentally characterized from the DC and noise viewpoint. A more realistic model of these devices was obtained in this work, an electrical and thermal model previously developed have been improved in such a way that noise effects are also included. We have implemented the model in a circuit simulator and reproduced the experimental measurements accurately. This provides a more realistic and complete tool for circuit design where magnetoresistive elements are combined with well-known complementary metal–oxide–semiconductor modules.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

NASA NTRS1982

Analysis of geologic terrain models for determination of optimum SAR sensor configuration and optimum information extraction for exploration of global non-renewable resources. Pilot study: Arkansas Remote Sensing Laboratory, part 1, part 2, and part 3

Computer-generated radar simulations and mathematical geologic terrain models were used to establish the optimum radar sensor operating parameters for geologic research. An initial set of mathematical geologic terrain models was created for three basic landforms and families of simulated radar images were prepared from these models for numerous interacting sensor, platform, and terrain variables. The tradeoffs between the various sensor parameters and the quantity and quality of the extractable geologic data were investigated as well as the development of automated techniques of digital SAR image analysis. Initial work on a texture analysis of SEASAT SAR imagery is reported. Computer-generated radar simulations are shown for combinations of two geologic models and three SAR angles of incidence.

Kaupp, V. H.↗

DOE OSTI2018

Modeling radiation damage to pixel sensors in the ATLAS detector

Silicon pixel detectors are at the core of the current and planned upgrade of the ATLAS detector at the Large Hadron Collider (LHC) . As the closest detector component to the interaction point, these detectors will be subject to a significant amount of radiation over their lifetime: prior to the High-Luminosity LHC (HL-LHC) [1], the innermost layers will receive a fluence in excess of 1015 neq/cm2 and the HL-LHC detector upgrades must cope with an order of magnitude higher fluence integrated over their lifetimes. Simulating radiation damage is essential in order to make accurate predictions for current and future detector performance that will enable searches for new particles and forces as well as precision measurements of Standard Model particles such as the Higgs boson. We present a digitization model that includes radiation damage effects on the ATLAS pixel sensors for the first time. In addition to thoroughly describing the setup, we present first predictions for basic pixel cluster properties alongside early studies with LHC Run 2 proton-proton collision data.

Ducourthial, A.↗

DOE OSTI2015

Modeling the dark current histogram induced by gold contamination in complementary-metal-oxide-semiconductor image sensors

We propose a model for dark current induced by metallic contamination in a CMOS image sensor. Based on Shockley-Read-Hall kinetics, the expression of dark current proposed accounts for the electric field enhanced emission factor due to the Poole-Frenkel barrier lowering and phonon-assisted tunneling mechanisms. To that aim, we considered the distribution of the electric field magnitude and metal atoms in the depth of the pixel. Poisson statistics were used to estimate the random distribution of metal atoms in each pixel for a given contamination dose. Then, we performed a Monte-Carlo-based simulation for each pixel to set the number of metal atoms the pixel contained and the enhancement factor each atom underwent, and obtained a histogram of the number of pixels versus dark current for the full sensor. Excellent agreement with the dark current histogram measured on an ion-implanted gold-contaminated imager has been achieved, in particular, for the description of the distribution tails due to the pixel regions in which the contaminant atoms undergo a large electric field. The agreement remains very good when increasing the temperature by 15 °C. We demonstrated that the amplification of the dark current generated for the typical electric fields encountered in the CMOS image sensors, which depends on the nature of the metal contaminant, may become very large at high electric field. The electron and hole emissions and the resulting enhancement factor are described as a function of the trap characteristics, electric field, and temperature.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2026

Advanced Facility Design and AI/ML Enabled Safeguards to Establish Secure, Economical Recycling of Fast Reactor Fuels (Final Scientific/Technical Report)

The project, "Advanced Facility Design and AI/ML Enabled Safeguards to Establish Secure, Economical Recycling of Fast Reactor Fuels," represents a significant advancement in nuclear fuel recycling technology. It integrates cutting-edge multimodal sensor fusion, machine learning (ML), and digital twin (DT) technologies to address challenges in material safeguarding, process optimization, and regulatory compliance for pyroprocessing facilities. This research has significantly enhanced the understanding of pyrochemical fuel recycling processes by developing innovative tools and methodologies. The Multimodal Safeguards Monitoring Unit (MSMU) combines electroanalytical techniques, Raman spectroscopy, and differential thermal analysis (DTA) to enable high-fidelity, near-real-time material accountancy measurements. Machine learning techniques, such as Long Short-Term Memory (LSTM) autoencoders, are utilized to detect anomalies in material balances and sensor data, improving the reliability of safeguards monitoring. Additionally, digital twin technology has been established to provide real-time system-level monitoring and diagnostics, integrating physics-based models with sensor data to optimize process safety and efficiency.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

NASA NTRS1983

Remote sensing of row crop structure and component temperatures using directional radiometric temperatures and inversion techniques

A physically based sensor response model of a row crop was used as the mathematical framework from which several inversion strategies were tested for extracting row structure information and component temperatures using a series of sensor view angles. The technique was evaluated on ground-based radiometric thermal infrared data of a cotton row crop that covered 48 percent of the ground in the vertical projection. The results showed that the accuracies of the predicted row heights and widths, vegetation temperatures, and soil temperatures of the cotton row crop were on the order of 5 cm, 1 deg, and 2 deg C, respectively. The inversion techniques can be applied to directional sensor data from aircraft platforms and even space platforms if the effects of atmospheric absorption and emission can be corrected. In theory, such inversion techniques can be applied to a wide variety of vegetation types and thus can have significant implications for remote sensing research and applications in disciplines that deal with incomplete vegetation canopies.

Kimes, D. S.↗

NASA NTRS2007

POST2 End-To-End Descent and Landing Simulation for the Autonomous Landing and Hazard Avoidance Technology Project

The Program to Optimize Simulated Trajectories II (POST2) is used as a basis for an end-to-end descent and landing trajectory simulation that is essential in determining the design and performance capability of lunar descent and landing system models and lunar environment models for the Autonomous Landing and Hazard Avoidance Technology (ALHAT) project. This POST2-based ALHAT simulation provides descent and landing simulation capability by integrating lunar environment and lander system models (including terrain, sensor, guidance, navigation, and control models), along with the data necessary to design and operate a landing system for robotic, human, and cargo lunar-landing success. This paper presents the current and planned development and model validation of the POST2-based end-to-end trajectory simulation used for the testing, performance and evaluation of ALHAT project system and models.

Fisher, Jody l.↗

NASA NTRS2016

Modeling SMAP Spacecraft Attitude Control Estimation Error Using Signal Generation Model

Two ground simulation software are used to model the SMAP spacecraft dynamics. The CAST software uses a higher fidelity model than the ADAMS software. The ADAMS software models the spacecraft plant, controller and actuator models, and assumes a perfect sensor and estimator model. In this simulation study, the spacecraft dynamics results from the ADAMS software are used as CAST software is unavailable. The main source of spacecraft dynamics error in the higher fidelity CAST software is due to the estimation error. A signal generation model is developed to capture the effect of this estimation error in the overall spacecraft dynamics. Then, this signal generation model is included in the ADAMS software spacecraft dynamics estimate such that the results are similar to CAST. This signal generation model has similar characteristics mean, variance and power spectral density as the true CAST estimation error. In this way, ADAMS software can still be used while capturing the higher fidelity spacecraft dynamics modeling from CAST software.

Soil Moisture Active Passive↗

DOE OSTI2017

Analytical Solutions of Transient Drift-Diffusion in P-N Junction Pixel Sensors

Radiation detection in applications ranging from high energy physics to medical imaging rely on solid state detectors, often hybrid pixel detectors with (1) reverse biased p-n junction pixel sensors and (2) readout ASICs, attached by flip chip-bonding. Transient signals characteristics are important in, e.g., matching ASIC and sensor design, modeling and optimizing detector parameters and describing timing and charge sharing properties. Currently analytical forms of transient signals are available for only a few limited cases (e.g., drift or diffusion) or for the steady state (which is not relevant for high energy radiation detection). Tools are available for (relatively slow) numerical evaluation of the transient charge transport. We present here the first analytical solutions of partial differential equations de scribing drift-diffusion-recombination charge transport in planar p-n junction sensors in a variety of conditions: (1) undepleted, (2) fully depleted, (3) taking into account the gradual velocity saturation, and (4) overdepleted. We deduce the Green’s functions which can be applied to any detection problem through simple convolution with the initial conditions. We compare the analytical solutions with Monte Carlo simulations and industry standard simulations (Synopsys Sentaurus), demonstrating good agreement. Using the analytical equations enables fast modeling of the influence of various detector parameters on tracking, imaging and timing performance, describing performance and enabling optimizations for different applications. Finally, we illustrate this model with applications in 3D+T (x,y,z,time) photon tracking and 4D+T (x,y,θ,φ,time) relativistic charged particle tracking.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗