Search NASA⌕ Search

SEARCH · Search NASA

Results for “process data”

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 469 records · Page 26

NIMBUS-5 sounder data processing system. Part 2: Results

The Nimbus-5 spacecraft carries infrared and microwave radiometers for sensing the temperature distribution of the atmosphere. Methods developed for obtaining temperature profiles from the combined set of infrared and microwave radiation measurements are described. Algorithms used to determine (a) vertical temperature and water vapor profiles, (b) cloud height, fractional coverage, and liquid water content, (c) surface temperature, and (d) total outgoing longwave radiation flux are described. Various meteorological results obtained from the application of the Nimbus-5 sounding data processing system during 1973 and 1974 are presented.

Smith, W. L.↗

A Model-Based Study of On-Board Data Processing Architecture for Balloon-Borne Aurora Observation

This paper discusses an application of ISAAC design methodology to a balloon-borne payload electronic system for aurora observation. The methodology is composed of two phases, high level design and low level implementation, the focus of this paper is on the high level design. This paper puts the system architecture in the context of a balloon based application but it can be generalized to any airborne/space-borne application. The system architecture includes a front-end detector, its corresponding data processing unit, and a controller. VisualSim has been used to perform modeling and simulations to explore the entire design space, finding optimal solutions that meet system requirements.

modeling↗

Digital data processing of ERTS-1 imagery of Delaware Bay

A user-oriented Earth Resources Data Center is in operation at Bendix Aerospace, which a capable of automatically producing classification imagery and thematic maps from ERTS computer compatible tapes. The ERDC is now being used to process ERTS-1 data under a subcontract from the University of Delaware. ERTS coverage of Delaware Bay has been processed to produce classification imagery of water masses in the bay, and a map of all land-water boundaries along the shoreline including wetlands and inland lakes. Imagery, the user's video monitor color display, the data processing facility, and methodology are described.

Conrod, A. C.↗

Optical-data-processing properties of a liquid-crystal television spatial light modulator

The potential of an extremely inexpensive liquid-crystal television (LCTV) as a two-dimensional spatial light modulator has been investigated. The LCTV modulates the transmission of coherent or incoherent light and can either be electronically addressed through a microcomputer or optically addressed with a TV camera. The transmission characteristics of the device have been measured, its diffraction pattern is examined, and its use as an input device for an optical correlator is tested. It is discovered that, with proper modifications, it has potential for optical-data-processing applications.

Liu, H.-K.↗

2.3.3.404 - National Lab and University Collaboration for MHK Instrumentation and Data Processing Tools

Field and laboratory validation, testing, demonstration, and operation are critical steps for increasing the technology readiness level of marine energy (ME) converters because they provide high-quality testing and performance data that are critical information used to feed all aspects of technology development. This project, in partnership with industry, enables the marine and hydrokinetic energy (MHK) community to reliably and efficiently collect, process, manage, and share quality data by facilitating access to and development of instrumentation, guidelines and data processing/QA tools. Under this project, open-source data processing code (MHKiT) and tools (ME Data Pipeline, MRE Code Hub, PRIMRE Code Catalog), instrumentation (loads measurements), data acquisition systems (miniDAQ), and measurement guidance tools (Telesto, high EMI guidance) were developed to facilitate the collection and processing of quality laboratory and field data. Overall, this project is intended to improve the quality of the data collected during laboratory and field demonstration projects by standardizing the collection and processing techniques, as well as by improving access to instrumentation, code, and measurement guidance. Quality data will, in turn, lead to improved knowledge capture following ME device testing.

data processing↗

Machine-Protection System for the PIP-II Linear Accelerator: Architecture and Data Processing Schemes

PIP-II at Fermilab features a brand-new, 800 MeV leading-edge Linear Accelerator (Linac) that will enable the Fermilab complex to deliver more than a megawatt of beam power to the Long Base-line Neutrino Facility (LBNF), requiring a robust Machine Protection System (MPS) to prevent beam-induced damage to delicate cryomodules and vacuum components. Signals in various accelerator components, in both analog and digital format, are collected and processed to assest the operating conditions of the accelerator. In the situation a potential damage is foreseen, the beam permit signal will be drop. A critical element of this system is the fast Analog Machine Protection System, a high-bandwidth platform designed for real-time beam loss monitoring. The system utilizes a modular, FPGA-based architecture to digitize signals from beam-sensing devices, such as AC Current Transformers (ACCTs), non-invasive Ring Pickups (RPUs) and beam scrapers. To eliminate both high frequency as well as 60 Hz noise that might be picked up in the industrial environment, data processing schemes beyond regular digital filtering are planned and implemented.

Wu, Jinyuan [Fermilab] (ORCID:0000000344329521)↗

Localized Defect Detection from Spatially Mapped, In-Situ Process Data With Machine Learning

In powder bed fusion additive manufacturing, machines are often equipped with in-situ sensors to monitor the build environment as well as machine actuators and subsystems. The data from these sensors offer rich information about the consistency of the fabrication process within a build and across builds. This information may be used for process monitoring and defect detection; however, little has been done to leverage this data from the machines for more than just coarse-grained process monitoring. In this work we demonstrate how these inherently temporal data may be mapped spatially by leveraging scan path information. We then train a XGBoost machine learning model to predict localized defects—specifically soot–using only the mapped process data of builds from a laser powder bed fusion process as input features. The XGBoost model offers a feature importance metric that will help to elucidate possible relationships between the process data and observed defects. Finally, we analyze the model performance spatially and rationalize areas of greater and lesser performance.

3D printing↗

Advanced technology for earth observation - Data processing

The Experimental Land Observing System (ELOS) is to demonstrate major advances in space-based land observation. Program success will depend upon successful demonstration of both space-segment performance and ground-system capabilities. Increases in data-rate, volume, and processing requirements present substantial challenges to the ground data processing system. ELOS is based on the utilization of the multispectral linear array (MLA) sensor, a solid-state, electronically scanned instrument. Current MLA instrument designs offer a number of capabilities beyond those provided in the Landsat imaging devices. These capabilities include greater resolution, inherent registration, onboard calibration, off-nadir pointing capability, and fore/aft pointing capability. The proposed MLA flight mission scenario will be one of continued growth and will include flights on the Space Transportation System. Attention is also given to ground-to-ground data transport, data capture, and the use of high speed computers.

Heffner, P.↗

Utilizing the Dynamic Networks Data Processing and Analysis Experiment (DNE18) to Establish Methodologies for the Comparison of Automatic Infrasonic Signal Detectors

The Dynamic Networks Experiment 2018 (DNE18) was a collaborative effort between Los Alamos National Laboratory (LANL), Sandia National Laboratories (SNL), Lawrence Livermore National Laboratory (LLNL) and Pacific Northwest National Laboratory (PNNL) designed to evaluate methodologies for multi-modal data ingestion and processing. One component of this virtual experiment was a quantitative assessment of current capabilities for infrasound data processing, beginning with the establishment of a baseline for infrasound signal detection. To produce such baselines, SNL and LANL exploited a common dataset of infrasound data recorded across a regional network in Utah from December 2010 through February 2011. We utilize two automated signal detectors, the Adaptive F-Detector (AFD) and the Multivariate Adaptive Learning Detector (MALD) to produce automated signal detection catalogs and an analyst-produced catalog. Comparisons indicate that automatic detectors may be able to identify small amplitude, low SNR events that cannot be identified by analyst review. We document detector performance in terms of precision and recall, demonstrating that the AFD is more precise, but the MALD has higher recall. We use a synthetic dataset of signals embedded in pink noise in order to highlight shortcomings in assessing detection algorithms for low signal to noise ratio signals which are commonly of interest to the nuclear monitoring community. For comparisons utilizing the synthetic dataset, the AFD has higher recall while precision is equal for both detectors. These results indicate that both detectors perform well across a variety of background noise environments; however, both detectors fail to identify repetitive, short duration signals arriving from similar backazimuths. These failures represent specific scenarios that could be targeted for further detector development.

97 MATHEMATICS AND COMPUTING↗

The 1971 corn blight watch experiment data processing, analysis, and interpretation

The principle data acquisition centers are listed for the corn blight experiment. A data storage and retrieval system was established. The purpose of the system was to: (1) store data in organized library; (2) maintain a record of all data stored and reduced for future access; (3) report the data in simplest form to users; and (4) record data reduction results from photographic and multispectral scanner sensors. The procedures designed for handling the large amounts of data are also reviewed.

Phillips, T. L.↗

S-193 scatterometer transfer function analysis for data processing

A mathematical model for converting raw data measurements of the S-193 scatterometer into processed values of radar scattering coefficient is presented. The argument is based on an approximation derived from the Radar Equation and actual operating principles of the S-193 Scatterometer hardware. Possible error sources are inaccuracies in transmitted wavelength, range, antenna illumination integrals, and the instrument itself. The dominant source of error in the calculation of scattering coefficent is accuracy of the range. All other ractors with the possible exception of illumination integral are not considered to cause significant error in the calculation of scattering coefficient.

Johnson, L.↗

EOS MLS Level 1B Data Processing, Version 2.2

A computer program performs level- 1B processing (the term 1B is explained below) of data from observations of the limb of the Earth by the Earth Observing System (EOS) Microwave Limb Sounder (MLS), which is an instrument aboard the Aura spacecraft. This software accepts, as input, the raw EOS MLS scientific and engineering data and the Aura spacecraft ephemeris and attitude data. Its output consists of calibrated instrument radiances and associated engineering and diagnostic data. [This software is one of several computer programs, denoted product generation executives (PGEs), for processing EOS MLS data. Starting from level 0 (representing the aforementioned raw data, the PGEs and their data products are denoted by alphanumeric labels (e.g., 1B and 2) that signify the successive stages of processing.] At the time of this reporting, this software is at version 2.2 and incorporates improvements over a prior version that make the code more robust, improve calibration, provide more diagnostic outputs, improve the interface with the Level 2 PGE, and effect a 15-percent reduction in file sizes by use of data compression.

Perun, Vincent↗

Integration of Nuclear Material Accounting Data and Process Monitoring Data for Improvement on Detection Probability in Safeguarding Electrochemical Processing Facilities (Final Technical Report)

The KAERI advanced spent fuel conditioning process (ACP) process is a critical component of the US- South Korean nuclear cooperation and the following “123 Agreement.” Its development has received considerable attention in both countries. The ACP is an electrochemical processing (pyroprocessing) that recycles over 96% of the used nuclear fuel (UNF). It is also intrinsically proliferation-resistant in theory. In normal operation, the U/TRU product is very hot radiologically. In addition, the Cm provides a high level of spontaneous neutrons, making the product unsuitable for weapon use. However, as pointed in some study, “the need for safeguards to protect against the diversion and misuse of separated plutonium applies essentially equally to all grades of plutonium.” As pointed by many studies, the well-established traditional Nuclear Material Accounting (NMA) approach cannot be directly applied to electrochemical processing because of the lack of an input accountability tank, the non-continuous material flow, and the unsatisfactory level of confidence in sampling methods. Therefore, nuclear safeguards remain a grand challenge in the developing of commercial electrochemical separations facilities, especially around the heart of such facilities, the electrorefiner (ER) systems. In contrast to NMA data, process monitoring (PM) data is normally an indirect measurement of the SNM and is acquired much more frequently. In a broad sense, PM includes monitoring by various types of equipment, e.g. radiation detectors, cameras, voltage, current sensors. Because it is already being collected by the operator, the additional cost to safeguards is low. It has long been believed that PM data can supplement NMA data and help improve safeguards, although the benefits are hard to quantify. The U.S. DOE’s Material Protection, Accounting, and Control Technology (MPACT) campaign has made substantial investments into innovative PM sensor technology and predictive model development for real- or near real-time measurement and prediction of molten salt density and level, salt composition and actinide concentration especially Pu, the cell voltage, and the cell current to supplement traditional NMA. For aqueous-based reprocessing facilities, it is reported that PM, integrated with traditional NMA, have a high detection probability for specific diversions. For electrochemical reprocessing, preliminary studies have shown that PM data can support traditional NMA in various ways by providing a basis to estimate some of the in-processing nuclear material inventories. Despite early success, further studies on fusion of PM data and NMA data are still needed, which is the goal of this proposed work.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Lunar laser ranging data processing in a Unix/X windows environment

In cooperation with the NASA Crustal Dynamics Project initiative placing workstation computers at each of its laser ranging stations to handle data filtering and normalpointing, MLRS personnel have developed a new generation of software to provide the same services for the lunar laser ranging data type. The Unix operating system and X windows/Motif provides an environment for both batch and interactive filtering and normalpointing as well as prediction calculations. The goal is to provide a transportable and maintainable data reduction environment. This software and some sample displays are presented. that the lunar (or satellite) datacould be processed on one computer while data was taken on the other. The reduction of the data was totally interactive and in no way automated. In addition, lunar predictions were produced on-site, another first in the effort to down-size historically mainframe-based applications. Extraction of earth rotation parameters was at one time attempted on site in near-realtime. In 1988, the Crustal Dynamics Project SLR Computer Panel mandated the installation of Hewlett-Packard 9000/360 Unix workstations at each NASA-operated laser ranging station to relieve the aging controller computers of much of their data and communications handling responsibility and to provide on-site data filtering and normal pointing for a growing list of artificial satellite targets. This was seen by MLRS staff as an opportunity to provide a better lunar data processing environment as well.

Ricklefs, Randall L.↗