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All sourcesNASA NTRSDOE OSTI
NASA NTRS1982

Modeling misregistration and related effects on multispectral classification

Any noise in measurements (due to the scene, sensor, or the analog to digital process) causes a finite fraction of measurements to fall outside of the classification limits. For field boundaries, where the misregistration effects are felt, the misregistration causes the border in a given (set of) band(s) to be closer than expected to a given pixel, so that the mixed materials in the pixels cause additional pixels to fall outside of the class limits. Considerations of the transient distance involved in the difference in brightness between adjacent fields, when scaled to "per pixel", allow the estimation of the width of the border zones. The entire problem is then scaled to field sizes to allow estimation of the global effects. This approach allows the estimation of the accuracy of multispectral classification which might be expected for field interiors, the useful number of quantization bits, and one set of criteria for an unbiased classifier.

Billingsley, F. C.↗
NASA NTRS1981

Model degradation effects on sensor failure detection

This paper discusses the effects of imperfect modeling on the detection and isolation of sensor failures. For systems with non-zero set points, deterministic inputs or non-zero noise biases, the model mismatch appears as a bias on the stochastic innovation process. This bias, if left unaccounted for, would be sufficient to declare a false alarm failure in one or more sensors. A practical design procedure based upon the Generalized Likelihood Ratio (GLR) form uses a finite data window sequential t-test to detect and isolate model mismatch effects and soft sensor failures. Application to an eighth order model of the QCSEE turbofan engine is discussed.

Leininger, G. G.↗
NASA NTRS1981

Texas Instruments /TI/ 800 x 800 charge-coupled device /CCD/ image sensor

Very-large area high-performance CCD image sensors with 800 x 800 pixel format have been successfully fabricated and operated on the basis of a three-level polysilicon gate technology. They are thinned to 8 microns over the entire 12.2 x 12.2 mm active area, and are used in the rear illumination mode. The light transfer characteristic has a gamma value of 1.000 + or - 0.002 over most of the dynamic range. Analysis of the noise behavior shows that the device SNR is shot-noise-limited over most of the dynamic range. Simple on-chip signal processing can be performed using an integration well to noiselessly collect signal charge from multiple pixels prior to reading out the charge. A UV-sensitive phosphor has been applied to the chip, yielding a device capable of imaging at wavelengths from the vacuum UV to the near IR.

Blouke, M. M.↗
NASA NTRS1982

Honeywell multispectral linear array (MLA) instrument design A system overview

In support of NASA's Experimental Land Observing System, a simple, reliable and relatively inexpensive multispectral linear array (MLA) instrument concept has been developed. The design satisfies requirements for multichannel radiometry and wide field imaging. A reflective off-axis anastigmat telescope is mounted in a highly strength-to-weight efficient truss structure, coupled to a solid beamsplitter assembly. Attention is given to the optical design of the MLA instrument, the telescope structure, the detector arrays, detector cooling, on-orbit calibration, signal processing electronics, the spacecraft interfaces, the modulation transfer function, the signal-to-noise ratio, radiometric accuracy, and spatial registration.

Eyring, D.↗
NASA NTRS1986

Modeling, estimation and identification methods for static shape determination of flexible structures

This paper outlines methods for modeling, identification and estimation for static determination of flexible structures. The shape estimation schemes are based on structural models specified by (possibly interconnected) elliptic partial differential equations. The identification techniques provide approximate knowledge of parameters in elliptic systems. The techniques are based on the method of maximum-likelihood that finds parameter values such that the likelihood functional associated with the system model is maximized. The estimation methods are obtained by means of a function-space approach that seeks to obtain the conditional mean of the state given the data and a white noise characterization of model errors. The solutions are obtained in a batch-processing mode in which all the data is processed simultaneously. After methods for computing the optimal estimates are developed, an analysis of the second-order statistics of the estimates and of the related estimation error is conducted. In addition to outlining the above theoretical results, the paper presents typical flexible structure simulations illustrating performance of the shape determination methods.

Rodriguez, G.↗
NASA NTRS1994

Dual Overlapping, Weighted Preaveragers

Improved technique incorporated into digital processing of received radio signals that bear digital data reduces bandwidth of incoming noise when receiver operated over broad range of potential signal bandwidths. Technique in question is updated version of older preaveraging technique; two modifications decrease vulnerability to interference from adjacent channels.

Poklemba, John J.↗
NASA NTRS1992

Headquarters summary reports

The status is summarized of the NASA sponsored involvement in high speed civil transport research and technology, including major cooperative efforts. That involvement is currently focussed on the High Speed Research Program. The program goals are an acceptable level of ozone depletion or sonic boom, the definition of which is a regulatory and political process. The HSRP goal is to provide technical bases for acceptability criteria. Community noise is currently regulated, and it seems clear that HSCT aircraft will have to comply with at least the spirit of the current subsonic constraint, FAR 36, Stage 3.

Source record↗
NASA NTRS1994

MEM application to IRAS CPC images

A method for applying the Maximum Entropy Method (MEM) to Chopped Photometric Channel (CPC) IRAS additional observations is illustrated. The original CPC data suffered from problems with repeatability which MEM is able to cope with by use of a noise image, produced from the results of separate data scans of objects. The process produces images of small areas of sky with circular Gaussian beams of approximately 30 in. full width half maximum resolution at 50 and 100 microns. Comparison is made to previous reconstructions made in the far-infrared as well as morphologies of objects at other wavelengths. Some projects with this dataset are discussed.

Marston, A. P.↗
NASA NTRS1999

Modeling the High Speed Research Cycle 2B Longitudinal Aerodynamic Database Using Multivariate Orthogonal Functions

The data for longitudinal non-dimensional, aerodynamic coefficients in the High Speed Research Cycle 2B aerodynamic database were modeled using polynomial expressions identified with an orthogonal function modeling technique. The discrepancy between the tabular aerodynamic data and the polynomial models was tested and shown to be less than 15 percent for drag, lift, and pitching moment coefficients over the entire flight envelope. Most of this discrepancy was traced to smoothing local measurement noise and to the omission of mass case 5 data in the modeling process. A simulation check case showed that the polynomial models provided a compact and accurate representation of the nonlinear aerodynamic dependencies contained in the HSR Cycle 2B tabular aerodynamic database.

Morelli, E. A.↗
NASA NTRS2001

Method and Apparatus for Determining Position Using Global Positioning Satellites

A global positioning satellite receiver having an antenna for receiving a L1 signal from a satellite is presented. The L1 signal is processed by a preamplifier stage including a band pass filter and a low noise amplifier and output as a radio frequency (RF) signal. A mixer receives and de-spreads the RF signal in response to a pseudo-random noise code, i,e., Gold code, generated by an internal pseudo-random noise code generator. A microprocessor enters a code tracking loop, such that during the code tracking loop, it addresses the pseudorandom code generator to cause the pseudo-random code generator to sequentially output pseudo-random codes corresponding to satellite codes used to spread the L1 signal, until correlation occurs. When an output of the mixer is indicative of the occurrence of correlation between the RF signal and the generated pseudo-random codes, the microprocessor caters an operational state which slows the receiver code sequence to stay locked with the satellite cede sequence. The output of the mixer is provided to a detector which, in turn, controls certain routines of the microprocessor. The microprocessor will output pseudo range information according to an interrupt routine in response detection of correlation. The pseudo range information is to be telemetered to a ground station which determines the position of the global positioning satellite receiver.

John L Ward↗
NASA NTRS1998

Method and apparatus for determining position using global positioning satellites

A global positioning satellite receiver having an antenna for receiving a L1 signal from a satellite. The L1 signal is processed by a preamplifier stage including a band pass filter and a low noise amplifier and output as a radio frequency (RF) signal. A mixer receives and de-spreads the RF signal in response to a pseudo-random noise code, i.e., Gold code, generated by an internal pseudo-random noise code generator. A microprocessor enters a code tracking loop, such that during the code tracking loop, it addresses the pseudo-random code generator to cause the pseudo-random code generator to sequentially output pseudo-random codes corresponding to satellite codes used to spread the L1 signal, until correlation occurs. When an output of the mixer is indicative of the occurrence of correlation between the RF signal and the generated pseudo-random codes, the microprocessor enters an operational state which slows the receiver code sequence to stay locked with the satellite code sequence. The output of the mixer is provided to a detector which, in turn, controls certain routines of the microprocessor. The microprocessor will output pseudo range information according to an interrupt routine in response detection of correlation. The pseudo range information is to be telemetered to a ground station which determines the position of the global positioning satellite receiver.

Ward, John↗
NASA NTRS2019

Modeling of a Modern Aircraft Through Calibration Techniques

NASA is seeking a new baseline aircraft model to assess the state-of-the-art technology for aircraft noise, emissions, and fuel/energy consumption as an update to a 2005 baseline. The process of modeling engine and airframe models as a system has historically required many iterations at NASA between the airframe and engine models. A new internal process presented in this paper contains a method that simultaneously calibrates an airframe and engine model to known data to create an aircraft system model. The work presented in this paper proposes a new framework in creating new aircraft models for future NASA research. This approach is presented as a general outline applicable to any chosen commercial aircraft. As an applied example, the B737 MAX 8 aircraft is chosen as the integrated engine and airframe model subjected to calibration. Initial results show a close match to available data but further refinement in the process is necessary for this ongoing work.

Thacker, Robert↗
NASA NTRS2021

Aerosol and Cloud Detection Using Machine Learning Algorithms and Space-Based Lidar Data

Clouds and aerosols play a significant role in determining the overall atmospheric radiation budget, yet remain a key uncertainty in understanding and predicting the future climate system. In addition to their impact on the Earth’s climate system, aerosols from volcanic eruptions, wildfires, man-made pollution events, and dust storms are hazardous to aviation safety and human health. Space-based lidar systems provide critical information about the vertical distributions of clouds and aerosols that greatly improve our understanding of the climate system. However, daytime data from backscatter lidars, such as the Cloud-Aerosol Transport System (CATS) on the International Space Station (ISS), must be averaged during science processing at the expense of spatial resolution to obtain sufficient signal-to-noise ratio (SNR) for accurately detecting atmospheric features. For example, 50% of all atmospheric features reported in daytime operational CATS data products require averaging to 60 km for detection. Furthermore, the single-wavelength nature of the CATS primary operation mode makes accurately typing these features challenging in complex scenes. This paper presents machine learning (ML) techniques that, when applied to CATS data, enable detection of atmospheric features during daytime operations with a horizontal resolution of 5 km compared to the 60 km horizontal resolution often required for daytime CATS data. A Convolutional Neural Network (CNN) trained using CATS standard data products also demonstrated the potential for improved cloud-aerosol discrimination compared to the operational CATS algorithms for cloud edges and complex near-surface scenes during daytime.

lidar↗
NASA NTRS2021

Aerosol and Cloud Detection Using Machine Learning Algorithms and Space-Based Lidar Data

Clouds and aerosols play a significant role in determining the overall atmospheric radiation budget, yet remain a key uncertainty in understanding and predicting the future climate system. In addition to their impact on the Earth’s climate system, aerosols from volcanic eruptions, wildfires, man-made pollution events and dust storms are hazardous to aviation safety and human health. Space-based lidar systems provide critical information about the vertical distributions of clouds and aerosols that greatly improve our understanding of the climate system. However, daytime data from backscatter lidars, such as the Cloud-Aerosol Transport System (CATS) on the International Space Station (ISS), must be averaged during science processing at the expense of spatial resolution to obtain sufficient signal-to-noise ratio (SNR) for accurately detecting atmospheric features. For example, 50% of all atmospheric features reported in daytime operational CATS data products require averaging to 60 km for detection. Furthermore, the single-wavelength nature of the CATS primary operation mode makes accurately typing these features challenging in complex scenes. This paper presents machine learning (ML) techniques that, when applied to CATS data, (1) increased the 1064 nm SNR by 75%, (2) increased the number of layers detected (any resolution) by 30%, and (3) enabled detection of 40% more atmospheric features during daytime operations at a horizontal resolution of 5 km compared to the 60 km horizontal resolution often required for daytime CATS operational data products. A Convolutional Neural Network (CNN) trained using CATS standard data products also demonstrated the potential for improved cloud-aerosol discrimination compared to the operational CATS algorithms for cloud edges and complex near-surface scenes during daytime.

lidar↗
DOE OSTI2019

Fundamental Advancements in Pre-Chamber Spark Ignition and Emissions Control for Natural Gas Engines

Argonne National Laboratory, the National Renewable Energy Laboratory, Oak Ridge National Laboratory, and Sandia National Laboratories collaborated on research to address barriers for natural gas engine efficiency. This research project focuses on early stage research focusing on pre-chamber spark-ignition (PCSI) to achieve diesel-like efficiency in medium- and heavy-duty natural gas engines by extending the lean dilution limit and/or exhaust gas recirculation (EGR) dilution limit, as well as shortening burn duration, with integrated aftertreatment. This project integrates experimental and simulation based tasks to address four key barriers to market penetration of PCSI for medium- and heavy-duty natural gas engines. These include Inadequate science base and simulation tools to describe/predict the fluid-mechanical and chemical-kinetic processes governing PCSI to enable engineers in industry to optimize designs for efficiency, noise, reliability, pollutant formation, emissions control integration, and drivability; limited ability to extend EGR and/or lean dilution limits at higher loads; increased propensity for PCSI hot-spot pre-ignition at high loads relative to spark ignition; and ineffective methane catalysts for the high engine-out unburned fuel concentrations coupled with low exhaust temperatures (<<400 degrees C) of high efficiency engines.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

THERMAL/STRUCTURAL ANALYSIS OF THE AXION QUANTUM METROLOGY CAVITY AND ITS COMPONENTS

This research was centered around maximizing the capability to cool dielectric material within a containment unit, or Photonic Band Gap (PBG) cavity, designed for detecting axion dark matter and identifying the unit’s thermal properties. There are multiple types of PBG cavities, but the latest version that axion researchers wish to use has been theorized to contain possible issues related to its thermal properties. Thermal conductivity is an issue with the dielectric material because it is made from alumina which is highly insulative. This is important since the research is being done in a cryogenic environment and the thermal noise affects the quantum bit used for detecting the axion to photon conversion process. Therefore, any improvements to this unit should be justified and implemented but are not entirely limited to thermal contact related aspects of PBG cavities. A prospect of using sapphire in place of alumina also exists, but this is a more expensive, less tested, and more elusive material to justify the creation of a full dielectric structure out of sapphire. Thermal aspects of the cavity were analyzed using finite element method (FEM) and an experiment designed to test different thermal joint materials. FEM was used to check the contraction of the cavity during cooling, the contact quality between the dielectric material and cavity wall, theheat flow rate through the assembly, and helps visualize the cavity’s reaction to different design changes. A simple comparison between thermal conductivity curves justified the usage of sapphire over alumina from a thermal property point of view. Thus, the method of experimentation had an additional dielectric material, sapphire, to test with and compare to alumina. The thermal test identified which material is best to use for a thermal joint but also simultaneously found the conductance of the joint and dielectric material as well as an estimate for what the temperature is inside the larger scale cavity since a thermometer cannot be placed inside the PBG when it cools. Initially, the cavity simulation was tested for structural deformation properties and stress distribution since it is made of copper which contracts heavily in comparison to most other materials. The reduction in volume of the cavity was less than anticipated which gave more room for a possible usage of a modified thermal joint for additional contact area and higher conductivity.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Detecting Process Equipment Failures Using Acoustic Data and Machine Learning

Nuclear power plant (NPP) process equipment such as fans, motors, valves, and pumps generate frequent or continuous noise, and deviations from the normal operational sounds made by this equipment can indicate potential issues. These deviations can be identified via automated acoustic anomaly detection, which involves using acoustic sensors (i.e., microphones) alongside detection algorithms to continuously monitor for changes in acoustic signatures. This task is made challenging by the substantial background noise that exists, such as operators opening and closing doors, manipulating valves, and conversing—in addition to typical plant noises. In collaboration with a nuclear power utility partner, this effort assessed the efficacy of acoustic anomaly detection when using a specific acoustic sensor that compresses data into a fixed set of features that are transferable over a standard Internet of Things communication protocol, thereby improving usability but potentially degrading detection performance. Two methods of performing automated acoustic anomaly detection were evaluated: one-class support vector machine (OC-SVM) and isolation forest (iForest). To enable the use of high-quality acoustic data encompassing both normal and anomalous conditions, the study utilized the publicly available Malfunctioning Industrial Machine Investigation and Inspection dataset, which includes real measured acoustic sensor data for a range of equipment types, model numbers, and signal-to-noise ratios (SNRs), along with a benchmark set of detection results. Using this dataset, the methods were tested and then compared against the benchmark results. The results indicated that although the specific acoustic sensor did not enable as rich a feature set extraction, the proposed methods with the limited feature set performed just as well. This provides solid justification for both the methods and the use of the proposed acoustic sensor.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗
DOE OSTI2015

Multivariate moment closure techniques for stochastic kinetic models

Stochastic effects dominate many chemical and biochemical processes. Their analysis, however, can be computationally prohibitively expensive and a range of approximation schemes have been proposed to lighten the computational burden. These, notably the increasingly popular linear noise approximation and the more general moment expansion methods, perform well for many dynamical regimes, especially linear systems. At higher levels of nonlinearity, it comes to an interplay between the nonlinearities and the stochastic dynamics, which is much harder to capture correctly by such approximations to the true stochastic processes. Moment-closure approaches promise to address this problem by capturing higher-order terms of the temporally evolving probability distribution. Here, we develop a set of multivariate moment-closures that allows us to describe the stochastic dynamics of nonlinear systems. Multivariate closure captures the way that correlations between different molecular species, induced by the reaction dynamics, interact with stochastic effects. We use multivariate Gaussian, gamma, and lognormal closure and illustrate their use in the context of two models that have proved challenging to the previous attempts at approximating stochastic dynamics: oscillations in p53 and Hes1. In addition, we consider a larger system, Erk-mediated mitogen-activated protein kinases signalling, where conventional stochastic simulation approaches incur unacceptably high computational costs.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗