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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Using Artificial Intelligence and Machine Learning to Enhance Mission Design and Operations of the Habitable Worlds Observatory (HWO)

One key aspect in the development of HWO is the early deployment of artificial intelligence (AI) and machine learning (ML) to enhance mission science and operations. Our subtask group is part of the HWO AI/ML working group and focuses on AI and ML for mission operations. Our task group seeks to educate other HWO working groups about AI and ML capabilities for mission operations, investigate how to bridge technology gaps, and enable new capabilities particularly in the areas of observational scheduling, instrument health monitoring, and downlink operations. We focus on mission tasking / scheduling both for mission analysis in development and operations. AI and ML for mission scheduling includes: tools to support proposal calls and review, ensuring fairness in calls for proposals, community peer reviews and ease workloads, as well as in-flight and ground software development (e.g., using natural language processing (NLP) to support process automation from requirements). AI and ML for the mission’s development and operations include 1) anomaly detection and prediction (from onboard and ground based tools) to monitor the spacecraft’s health, 2) ground-based automated scheduling for mission operations including long-term and short-term planning and maintenance, and 3) flight system flexible execution (as flight proven for Spitzer and JWST) to enable robust execution despite execution variations, and 4) data analysis for prioritization (e.g., real-time data evaluation leading to autonomous actions and adjustments, high-priority identification, onboard data compression, etc.). Incorporation of ML and AI will enable HWO to address the major science questions related to exoplanet characterization, general astrophysics, and solar system exploration and also extend the boundaries of space mission technologies.

Mark Moussa↗
NASA NTRS1985

Vision requirements for Space Station applications

Problems which will be encountered by computer vision systems in Space Station operations are discussed, along with solutions be examined at Johnson Space Station. Lighting cannot be controlled in space, nor can the random presence of reflective surfaces. Task-oriented capabilities are to include docking to moving objects, identification of unexpected objects during autonomous flights to different orbits, and diagnoses of damage and repair requirements for autonomous Space Station inspection robots. The approaches being examined to provide these and other capabilities are television IR sensors, advanced pattern recognition programs feeding on data from laser probes, laser radar for robot eyesight and arrays of SMART sensors for automated location and tracking of target objects. Attention is also being given to liquid crystal light valves for optical processing of images for comparisons with on-board electronic libraries of images.

Crouse, K. R.↗
NASA NTRS1985

Space station data system analysis/architecture study. Task 3: Trade studies, DR-5, volume 1

The primary objective of Task 3 is to provide additional analysis and insight necessary to support key design/programmatic decision for options quantification and selection for system definition. This includes: (1) the identification of key trade study topics; (2) the definition of a trade study procedure for each topic (issues to be resolved, key inputs, criteria/weighting, methodology); (3) conduct tradeoff and sensitivity analysis; and (4) the review/verification of results within the context of evolving system design and definition. The trade study topics addressed in this volume include space autonomy and function automation, software transportability, system network topology, communications standardization, onboard local area networking, distributed operating system, software configuration management, and the software development environment facility.

Source record↗
NASA NTRS2019

Predictive Modeling for Differential Diagnosis and Mortality Risk Assessment

The prevalence of electronic health record (EHR) systems has brought prodigious biomedical informatics opportunity. Automated machine learning methods can effectively utilize such data and have become common tools for healthcare predictive modeling. Researches in medical informatics have explored the potential of deep learning and classical models in emergent care scenarios. In particular, predicting differential diagnoses for admissions have proven useful in decreasing unnecessary lab tests and improving inpatient triage decision-making. Moreover, identification of high-risk patients for in-hospital mortality is vitally important to maximize allocation of medical resources.The Medical Information Mart for Intensive Care (MIMIC-III) database, containing de-identified critical care inpatient was used in our study. This data set captures hospital patient laboratory measurements, pharmacologic prescriptions, diagnostic data and procedure event recordings. When considering adult patients and discounting admissions with ICU length of stay less than 24 hours, there were 37,787 unique admissions and 30,414 total patients. We examined the top 25 most prevalent ICD-9 group-level disease specificities in MIMIC-III using a multi-label classification model. In-hospital mortality was modeled as binary classification with 4,155 (13%) adult patients that expired, of which 3,138 (75.5%) were in the ICU setting. The metrics AUC, F1 score, sensitivity and specificity values calculated for each disease label measured prediction performance.The usage of ICD-9 group codes reduced feature dimension from 14,567 to 942 and greatly improved distribution of patient diagnostic categories. Disease temporal patterns were captured by considering the most frequently sampled 6 vital signs and 13 laboratory values. Missing data were imputed at each time-stamp. Time-series raw hourly average values were converted into 5 summary features (mean, standard deviation, number of observations, min & max values). Patient demographic variables such as age, gender, marital status and ethnicity were also factored into the modeling. Choi et al showed that contextual embedding of medical data, diagnostic and procedural codes alone can predict future diagnoses with sensitivity as high as 0.79. We utilized an embedding technique called word2vec which allowed sparse representations of medical history to be transformed into dense word vectors. The mappings captured contextual information by treating each admission as a sentence and learning the most likely neighboring words in a sliding window fashion. Binary and multi-label classification was achieved via collapse models, which do not consider temporal information, as well as recurrent neural networks with regularization, Softmax output layer activation together with categorical cross-entropy as the loss function.

US Army collaboration↗
NASA NTRS1988

Automated extraction of absorption features from Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Geophysical and Environmental Research Imaging Spectrometer (GERIS) data

Automated techniques were developed for the extraction and characterization of absorption features from reflectance spectra. The absorption feature extraction algorithms were successfully tested on laboratory, field, and aircraft imaging spectrometer data. A suite of laboratory spectra of the most common minerals was analyzed and absorption band characteristics tabulated. A prototype expert system was designed, implemented, and successfully tested to allow identification of minerals based on the extracted absorption band characteristics. AVIRIS spectra for a site in the northern Grapevine Mountains, Nevada, have been characterized and the minerals sericite (fine grained muscovite) and dolomite were identified. The minerals kaolinite, alunite, and buddingtonite were identified and mapped for a site at Cuprite, Nevada, using the feature extraction algorithms on the new Geophysical and Environmental Research 64 channel imaging spectrometer (GERIS) data. The feature extraction routines (written in FORTRAN and C) were interfaced to the expert system (written in PROLOG) to allow both efficient processing of numerical data and logical spectrum analysis.

Kruse, Fred A.↗
NASA NTRSDate not supplied

Rapid Aero Modeling for Urban Air Mobility Aircraft in Wind-Tunnel Tests

Rapid Aero Modeling (RAM) applied to wind tunnel testing, RAM-T, is an approach to efficiently and automatically obtain aerodynamic models during testing. The approach saves time and resources by responding to the demand for experimental efficiency and model fidelity. Motivation for this demand is more acute when investigating a class of vehicles categorized as Urban Air Mobility (UAM) aircraft where many features from both aircraft and rotorcraft are present. RAM-T provides a feedback loop around the test facility to guide the test toward high-fidelity, statistically rigorous aircraft models. The general RAM approach is applicable to computational or physical experiments. It combines concepts from design of experiment theory and aircraft system identification theory that allow the user the freedom to choose, in advance of the test, a specific level of fidelity in terms of prediction error. RAM only collects data required to meet the user-specified fidelity and fidelity is only limited by the facility and test article capabilities. This paper presents results from tests conducted for development of an automated RAM-T technology. The results highlight some of the unique features of RAM applied to eVTOL configurations.

Aerodynamics↗
NASA NTRS2019

Ask-The-Expert: Minimizing Human Review for Big Data Analytics Through Active Learning

In this CIF project, we worked toward semi-automating knowledge discovery from anomaly detection algorithms through the use of active learning. Active learning is an area of research within machine learning that uses an "expert in the loop" to learn from large data sets that have very few annotations or labels available, and where providing such labels is expensive. In our case, the task can be defined as the identification of safety events from flight operational data. Since traditional anomaly detection algorithms cannot differentiate between operationally relevant and irrelevant statistical anomalies, Subject Matter Experts (SMEs) have a lengthy and expensive burden of investigating every example identified by the detection algorithm, classifying and labeling them as relevant or irrelevant. Active learningidentifies the unlabeled example for which a label would most improve the classifier, asks the domain expert for a label, and repeats this process until there are no more resources (time, budget) available for labeling or a minimum required performance is reached. A positive label indicates an operationally significant safety event whereas a negative label indicates otherwise. Based on these few labels we propose to build an active learning system that utilizes the SME's time in the most effective manner by iteratively asking for labels for as few informative instances as possible. Our work was proposed to be a stepping stone toward implementation and deployment of the system with user interface to be pursued by the Aviation Operations and Safety Program (AOSP) given its interest in safety monitoring and discovery of safety incidents.

aviation safety↗
NASA NTRS1996

Early-type galaxies: Automated reduction and analysis of ROSAT PSPC data

Preliminary results of early-type galaxies that will be part of a galaxy catalog to be derived from the complete Rosat data base are presented. The stored data were reduced and analyzed by an automatic pipeline. This pipeline is based on a command language scrip. The important features of the pipeline include new data time screening in order to maximize the signal to noise ratio of faint point-like sources, source detection via a wavelet algorithm, and the identification of sources with objects from existing catalogs. The pipeline outputs include reduced images, contour maps, surface brightness profiles, spectra, color and hardness ratios.

Mackie, G.↗
NASA NTRS1991

The interconnected data directory system and Catalog Interoperability

The Catalog Interoperability (CI) project, designed to enable rapid and efficient identification, location, and access to data of interest to the science community, is considered. The CI goal is to create a worldwide data information network composed of interconnected directory, guide (catalog), and inventory systems. Directories were created to aid in finding data. The directories contain brief information about the data sets, sufficient for determining whether further investigation is warranted. They also provide automated links to other information systems which give more detail on data of interest. A common format for describing data sets has been developed, called the Directory Interchange Format (DIF), which is used as the basis of information to be shared among the directories. These DIF files can be passed among the directories to keep their information up to date.

Thieman, James R.↗
NASA NTRS1987

Mapping hydrothermally altered rocks in the Northern Grapevine Mountains, Nevada and California with the airborne imaging spectrometer

Seven flightlines of Airborne Imaging Spectrometer (AIS) data were analyzed for an area of hydrothermally altered rocks. The data were reduced to reflectance relative to an average spectrum, and an automated procedure was used to produce a color coded image displaying absorption band information. Individual spectra were extracted from the AIS images to determine the detailed mineralogy. Two alteration types were mapped based upon mineralogy identified using the AIS data. The primary alteration type is quartz sericite pyrite alteration which occurs in northwest-trending zones in quartz monzonite porphyry. The AIS data allow identification of sericite (muscovite) based upon a strong absorption feature near 2.21 micron and weaker absorption features near 2.35 and 2.45 micron. The second alteration type occurs as a zone of argillic alteration associated with a granitic intrusion. Montmorillonite was identified based on a weak to moderate absorption feature near 2.2 micron and the absence of the two absorption features at longer wavelengths characteristic of sericite. Montmorillonite could be identified only where concentrations of sericite did not mask the montmorillonite spectrum.

Kruse, Fred A.↗
NASA NTRS1993

Experiments In Characterizing Vibrations Of A Structure

Report discusses experiments conducted to test methods of identification of vibrational and coupled rotational/vibrational modes of flexible structure. Report one in series that chronicle development of integrated system of methods, sensors, actuators, analog and digital signal-processing equipment, and algorithms to suppress vibrations in large, flexible structure even when dynamics of structure partly unknown and/or changing. Two prior articles describing aspects of research, "Autonomous Frequency-Domain Indentification" (NPO-18099), and "Automated Characterization Of Vibrations Of A Structure" (NPO-18141).

Yam, Yeung↗
NASA NTRS2004

Deployment of a Fast-GCMS System to Measure C2 to C5 Carbonyls, Methanol and Ethanol Aboard Aircraft

Through funding of this proposal, a fast response gas chromatograph/mass spectrometer (FGCMS) instrument to measure less than or equal to C4 carbonyl compounds and methanol was developed for the NASA GTE TRACE-P (Global Tropospheric Experiment, Transport And Chemical Evolution Over The Pacific) mission. The system consists of four major components: sample inlet, preconcentration system, gas chromatograph (GC), and detector. The preconcentration system is a custom-built cryogen-conservative system. The GC is a compact, custom-built unit that can be temperature programmed and rapidly cooled. Detection is accomplished with an Agilent Technologies 5973 mass spectrometer. The FGCMS instrument provides positive identification because the compounds are chromatographically separated and mass selected. During TRACE-P, a sample was analyzed every 5 minutes. The FGCMS limit of detection was between 5 and 75 pptv, depending on the compound. The entire instrument package is contained in a standard NASA instrument rack (106 cm x 61 cm x 135 cm), consumes less than 1200 watts and is fully automated with LabViEW 6i. Methods were developed or producing highly accurate gas phase standards for the target compounds and for testing the system in the presence of potential interferents. This report presents data on these tests and on the general overall performance of the system in the laboratory and aboard the DC-8 aircraft during the mission. Vertical profiles for acetaldehyde, methanol, acetone, propanal, methyl ethyl ketone, and butanal from FGCMS data collected over the entire mission are also presented.

Apel, Eric C.↗
NASA NTRS1971

Monitoring forest land from high altitude and from space

Forest inventory, forest stress, and standardization and calibration studies are presented. These include microscale photointerpretation of forest and nonforest land classes, multiseasonal film densities for automated forest and nonforest land classification, trend and spread of bark beetle infestations from 1968 through 1971, aerial photography for determining optimum levels of stand density to reduce such infestations, use of airborne spectrometers and multispectral scanners for previsual detection of Ponderosa pine trees under stress from insects and diseases, establishment of an earth resources technology satellite test site in the Black Hills and the identification of natural resolution targets, detection of root disease impact on forest stands by sequential orbital and suborbital multispectral photography, and calibration of color aerial photography.

Source record↗
NASA NTRS1997

Automated navigation assessment for earth survey sensors using island targets

An automated method has been developed for performing navigation assessment on satellite-based Earth sensor data. The method utilizes islands as targets which can be readily located in the sensor data and identified with reference locations. The essential elements are an algorithm for classifying the sensor data according to source, a reference catalog of island locations, and a robust pattern-matching algorithm for island identification. The algorithms were developed and tested for the Sea-viewing Wide Field-of-view Sensor (SeaWiFS), an ocean color sensor. This method will allow navigation error statistics to be automatically generated for large numbers of points, supporting analysis over large spatial and temporal ranges.

Patt, Frederick S.↗
NASA NTRS1997

An Automated Method for Navigation Assessment for Earth Survey Sensors Using Island Targets

An automated method has been developed for performing navigation assessment on satellite-based Earth sensor data. The method utilizes islands as targets which can be readily located in the sensor data and identified with reference locations. The essential elements are an algorithm for classifying the sensor data according to source, a reference catalogue of island locations, and a robust pattern-matching algorithm for island identification. The algorithms were developed and tested for the Sea-viewing Wide Field-of-view Sensor (SeaWiFS), an ocean colour sensor. This method will allow navigation error statistics to be automatically generated for large numbers of points, supporting analysis over large spatial and temporal ranges.

Patt, F. S.↗
NASA NTRS2020

Assessment of Holographic Microscopy for Quantifying Marine Particle Size and Concentration

Holographic microscopy has emerged as a tool for in situ imaging of microscopic organisms and other particles in the marine environment: appealing because of relatively larger sampling volumes and simpler optical configurations compared to similar imaging systems. However, its quantitative capabilities have remained uncertain, in part because hologram reconstruction and image recognition have required manual operation. Here we assess the quantitative skill of our automated hologram processing pipeline, the CCV Pipeline, to evaluate the size and concentration measurements of environmental and cultured assemblages of marine plankton particles, and microspheres. Over one-million particles, ranging from 10-200 microns equivalent spherical diameter (ESD), imaged by the 4-Deep HoloSea digital inline holographic microscope (DIHM) are analyzed. These measurements were collected in parallel with FlowCam, Imaging FlowCytobot, and manual microscope identification. Once corrections for particle location and nonuniform illumination were developed and applied, the DIHM showed an underestimate in ESD of about 3-10%, but successfully reproduced the size-spectral-slope from environmental samples, and the size distribution of monocultures and microspheres. DIHM concentrations (order 1-1000 particles~ml-1) showed linear agreement (r^2=0.73) with the other instruments, but individual comparisons at times had large uncertainty.

holograpy↗
NASA NTRS1992

Identification of high-level functional/system requirements for future civil transports

In order to accommodate the rapid growth in commercial aviation throughout the remainder of this century, the Federal Aviation Administration (FAA) is faced with a formidable challenge to upgrade and/or modernize the National Airspace System (NAS) without compromising safety or efficiency. A recurring theme in both the Aviation System Capital Investment Plan (CIP), which has replaced the NAS Plan, and the new FAA Plan for Research, Engineering, and Development (RE&D) rely on the application of new technologies and a greater use of automation. Identifying the high-level functional and system impacts of such modernization efforts on future civil transport operational requirements, particularly in terms of cockpit functionality and information transfer, was the primary objective of this project. The FAA planning documents for the NAS of the 2005 era and beyond were surveyed; major aircraft functional capabilities and system components required for such an operating environment were identified. A hierarchical structured analysis of the information processing and flows emanating from such functional/system components were conducted and the results documented in graphical form depicting the relationships between functions and systems.

Swink, Jay R.↗
NASA NTRS1988

A stereo triangulation system for structural identification: Analytical and experimental results

Identification of large space structures' distributed mass, stiffness, and energy dissipation characteristics poses formidable analytical, numerical, and implementation difficulties. Development of reliable on-orbit structural identification methods is important for implementing active vibration suppression concepts which are under widespread study in the large space structures community. Near the heart of the identification problem lies the necessity of making a large number of spatially distributed measurements of the structure's vibratory response and the associated force/moment inputs with sufficient spatial and frequency resolution. In the present paper, we discuss a method whereby tens of active or passive (retro-reflecting) targets on the structure are tracked simultaneously by the focal planes of two or more video cameras mounted on an adjacent platform. Triangulation (optical ray intersection) of the conjugate image centroids yield inertial trajectories of each target on the structure. Given the triangulated motion of the targets, we apply and extend methodology developed by Creamer, Junkins, and Juang to identify the frequencies, mode shapes, and updated estimates for the mass/stiffness/damping parameterization of the structure. The methodology is semi-automated, for example, the post experiment analysis of the video imagery to determine the inertial trajectories of the targets typically requires less than thirty minutes of real time. Using methodology discussed herein, the frequency response of a large number of points on the structure (where reflective targets are mounted) on the structure can be determined from optical measurements alone. For comparison purposes, we also utilize measurements from accelerometers and a calibrated impulse hammer. While our experimental work remains in a research stage of development, we have successfully tracked and stereo triangulated 20 targets (on a vibrating cantilevered grid structure) at a sample frequency of 200 HZ, and have established conclusively the feasibility and desirability of this approach. We discuss, in summary, recent advances in analog and digital video processing methodology, actuation methods, and bring them to bear on the structural identification problem. We include a brief discussion of our experimental hardware and some recent experimental results which support the practical feasibility of this structural vibration sensing approach.

Junkins, J. L.↗