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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
NASA NTRS2005

Generic Kalman Filter Software

The Generic Kalman Filter (GKF) software provides a standard basis for the development of application-specific Kalman-filter programs. Historically, Kalman filters have been implemented by customized programs that must be written, coded, and debugged anew for each unique application, then tested and tuned with simulated or actual measurement data. Total development times for typical Kalman-filter application programs have ranged from months to weeks. The GKF software can simplify the development process and reduce the development time by eliminating the need to re-create the fundamental implementation of the Kalman filter for each new application. The GKF software is written in the ANSI C programming language. It contains a generic Kalman-filter-development directory that, in turn, contains a code for a generic Kalman filter function; more specifically, it contains a generically designed and generically coded implementation of linear, linearized, and extended Kalman filtering algorithms, including algorithms for state- and covariance-update and -propagation functions. The mathematical theory that underlies the algorithms is well known and has been reported extensively in the open technical literature. Also contained in the directory are a header file that defines generic Kalman-filter data structures and prototype functions and template versions of application-specific subfunction and calling navigation/estimation routine code and headers. Once the user has provided a calling routine and the required application-specific subfunctions, the application-specific Kalman-filter software can be compiled and executed immediately. During execution, the generic Kalman-filter function is called from a higher-level navigation or estimation routine that preprocesses measurement data and post-processes output data. The generic Kalman-filter function uses the aforementioned data structures and five implementation- specific subfunctions, which have been developed by the user on the basis of the aforementioned templates. The GKF software can be used to develop many different types of unfactorized Kalman filters. A developer can choose to implement either a linearized or an extended Kalman filter algorithm, without having to modify the GKF software. Control dynamics can be taken into account or neglected in the filter-dynamics model. Filter programs developed by use of the GKF software can be made to propagate equations of motion for linear or nonlinear dynamical systems that are deterministic or stochastic. In addition, filter programs can be made to operate in user-selectable "covariance analysis" and "propagation-only" modes that are useful in design and development stages.

Lisano, Michael E., II↗
NASA NTRSDate not supplied

Classifying Agnostic Biosignatures using Raman, VNIR, and Elemental Data

How can we use our current wealth of terrestrial data, encompassing biogenic and abiogenic systems, to determine the distinguishing properties of life? SCOBI (Statistical Classification of Biosignature Information) uses machine learning techniques to algorithmically identify combinations of measurements that are “indicative of life”. A set of ~1000 observations, comprising elemental abundance, isotopic fractionation, VNIR reflectance, and (in progress) Raman spectra, have been assembled from existing literature and databases. The observations cover systems classified as “indicative alive” (e.g., cells, vegetation), “indicative non-alive” (e.g., fossils, teeth), “mixed indicative” (e.g., soil, pond water), or “non-indicative” (e.g., rocks, meteorites). VNIR data was preprocessed by linear interpolation from 400-2100 nm and smoothed with a Savitzky-Golay filter. To limit the amount of Earth-biochemistry-specific (non-agnostic) information included, the first five spectral features extracted were number of peaks, number of troughs, mean reflectance, mean peak width, and broadest peak width. To help further emphasize agnostic biosignatures, Earth-specific features such as chlorophylls have been manually flagged so that feature importance with and without them can be compared. Classifiers including k-nearest neighbors (KNN), Gaussian Naïve Bayes (GNB), logistic regression (LR), random forest (RF), and support vector machine (SVM) were implemented, as was a combination voting classifier. Performance metrics included false positive rates, false negative rates, and AUC with 50-50 test/train splits (Monte Carlo simulations). Key takeaways from this stage, prior to the inclusion of Raman spectra, are (1) the overall success rate of 0.933 AUC was most heavily influenced by the elemental abundance data; and (2) VNIR reflectance had the lowest classification performance with 0.52 AUC (58% of objects correctly classified). The next steps are to complete integration of Raman spectral data and to improve the approach to pre-processing and feature extraction for both types of spectral data, such as automated baseline removal, whole spectrum matching, and dimensionality reduction.

Biosignatures↗
DOE OSTI2022

Forecasting of in situ electron energy loss spectroscopy

Abstract Forecasting models are a central part of many control systems, where high-consequence decisions must be made on long latency control variables. These models are particularly relevant for emerging artificial intelligence (AI)-guided instrumentation, in which prescriptive knowledge is needed to guide autonomous decision-making. Here we describe the implementation of a long short-term memory model (LSTM) for forecasting in situ electron energy loss spectroscopy (EELS) data, one of the richest analytical probes of materials and chemical systems. We describe key considerations for data collection, preprocessing, training, validation, and benchmarking, showing how this approach can yield powerful predictive insight into order-disorder phase transitions. Finally, we comment on how such a model may integrate with emerging AI-guided instrumentation for powerful high-speed experimentation.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Why is the winner the best?

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multi- center study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), im- age preprocessing (97%), data curation (79%), and post- processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

Eisenmann, Matthias↗
DOE OSTI2024

Offshore Wind ENergy Simulation Toolkit (OWENS)

SAND2021-2751 O The Offshore Wind ENergy Simulation Toolkit (OWENS) is a collection of aerodynamic, structural, hydrodynamic, drivetrain, controls, composite structure and mesh preprocessing, and data postprocessing. OWENS is primarily an ontology, or glue code, pulling together many open-source and Sandia-developed libraries to model the aero-servo-hydro-elastic physics of wind and marine energy turbines. The toolkit’s intended use is for arbitrary aeroelastic rotor configurations analysis including vertical-axis wind turbines, horizontal-axis wind turbines, and analogous marine energy applications for fixed-bottom and floating configurations. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525

Owens, Brian↗
DOE OSTI2021

HAIMOS Ensemble Forecasts for Intra-day and Day- Ahead GHI, DNI and Ramps

The objective of this research is to develop a hybrid physics-based/data-driven forecast model to improve direct normal and global horizontal irradiance (DNI and GHI) prediction for horizons ranging from 1 to 72 hours. Project objectives also address key gaps in state-of-the-art solar forecasting: accurate probabilistic solar forecasts and the forecasting of large irradiance ramps (ramp onset and magnitude). The proposed model ensembles Numerical Weather Prediction (NWP) forecasts, determinist physics-based algorithms, and new-generation cloud cover products (high-resolution rapid refresh satellite images and Large Eddy Simulations). The result is the Hybrid Adaptive Input Model Objective Selection (HAIMOS) ensemble model. HAIMOS blends state of the art machine learning methodologies with physics-based models for cloud cover and cloud optical depth forecasts. The technical activities followed a two-pronged strategy. First, the preprocessing of data, the selection of inputs to the nonlinear approximators, the type of approximator and objective functions, and post-processing ensembling techniques included in HAIMOS were all optimized adaptively to find the best model for a specific goal (reduce DNI/GHI forecast error, improve the prediction of ramp onset, etc.). Second, a large effort was put in improving cloud identification and the forecast of cloud cover and cloud optical depth. To this end, new-generation cloud parametrization products were developed in this work. These include improved algorithms to assist in cloud identification, cloud classification and cloud parametrization from satellite images – three key factors in the accuracy of 1 to 6-hours irradiance forecasts and prediction of ramp onset. Furthermore, we also included cloud information extracted from high resolution rapid refresh satellite images (GOES-16) and Large Eddy Simulations (LES). LES was used to model the atmosphere in detail over locations of interest and produce cloud optical depth forecasts. Once these data streams were validated, they were used as input data to the HAIMOS forecast. The model was developed using data from several climatologically distinct locations with potential for high solar penetration. In the last year of the project, we conducted a validation campaign according to the guidelines stipulated by the Topic Area 1 project as described in the FOA. This effort brings, for the first time, proven machine-learning methodologies for generating state-of-the-art solar forecasts interweaved with detailed physics-based models for cloud detection, and cloud optical depth forecasts. HAIMOS will generate accurate irradiance probabilistic forecast to assist in reducing solar generation prediction error. Globally optimized solar forecast models are more likely to impact solar energy stakeholders. The goal of this project was to increase the state-of-the-art forecast skill from their present values of 10 to 35%. At the end of the project, we achieved between 30% and 50% forecast skill across a wide range of horizons for both GHI and DNI.

14 SOLAR ENERGY↗
NASA NTRS1977

Land use/land cover mapping (1:25000) of Taiwan, Republic of China by automated multispectral interpretation of LANDSAT imagery

Three methods were tested for collection of the training sets needed to establish the spectral signatures of the land uses/land covers sought due to the difficulties of retrospective collection of representative ground control data. Computer preprocessing techniques applied to the digital images to improve the final classification results were geometric corrections, spectral band or image ratioing and statistical cleaning of the representative training sets. A minimal level of statistical verification was made based upon the comparisons between the airphoto estimates and the classification results. The verifications provided a further support to the selection of MSS band 5 and 7. It also indicated that the maximum likelihood ratioing technique can achieve more agreeable classification results with the airphoto estimates than the stepwise discriminant analysis.

Sung, Q. C.↗
NASA NTRS1980

LAPR: An experimental aircraft pushbroom scanner

A three band Linear Array Pushbroom Radiometer (LAPR) was built and flown on an experimental basis by NASA at the Goddard Space Flight Center. The functional characteristics of the instrument and the methods used to preprocess the data, including radiometric correction, are described. The radiometric sensitivity of the instrument was tested and compared to that of the Thematic Mapper and the Multispectral Scanner. The radiometric correction procedure was evaluated quantitatively, using laboratory testing, and qualitatively, via visual examination of the LAPR test flight imagery. Although effective radiometric correction could not yet be demonstrated via laboratory testing, radiometric distortion did not preclude the visual interpretation or parallel piped classification of the test imagery.

Wharton, S. W.↗
NASA NTRS1981

LAPR - An experimental pushbroom scanner

A three-band Linear Array Pushbroom Radiometer (LAPR) was built and flown on an experimental basis by NASA, at the Goddard Space Flight Center. The functional characteristics of the instrument and the methods used to preprocess the data, including radiometric correction, are described. The radiometric sensitivity of the instrument was tested and compared to that of the Thematic Mapper and the Multispectral Scanner. The radiometric correction procedure was evaluated quantitatively, using laboratory testing, and qualitatively, by visual examination of the LAPR test flight imagery. Although effective radiometric correction could not yet be demonstrated by laboratory testing, radiometric distortion did not preclude the visual interpretation or classification of the test imagery.

Wharton, S. W.↗
NASA NTRS1981

Low-cost digital image processing at the University of Oklahoma

Computer assisted instruction in remote sensing at the University of Oklahoma involves two separate approaches and is dependent upon initial preprocessing of a LANDSAT computer compatible tape using software developed for an IBM 370/158 computer. In-house generated preprocessing algorithms permits students or researchers to select a subset of a LANDSAT scene for subsequent analysis using either general purpose statistical packages or color graphic image processing software developed for Apple II microcomputers. Procedures for preprocessing the data and image analysis using either of the two approaches for low-cost LANDSAT data processing are described.

Harrington, J. A., Jr.↗
NASA NTRS1981

Instructional image processing on a university mainframe: The Kansas system

An interactive digital image processing program package was developed that runs on the University of Kansas central computer, a Honeywell Level 66 multi-processor system. The module form of the package allows easy and rapid upgrades and extensions of the system and is used in remote sensing courses in the Department of Geography, in regional five-day short courses for academics and professionals, and also in remote sensing projects and research. The package comprises three self-contained modules of processing functions: Subimage extraction and rectification; image enhancement, preprocessing and data reduction; and classification. Its use in a typical course setting is described. Availability and costs are considered.

Williams, T. H. L.↗
NASA NTRS1982

Satellite remote sensing - An integral tool in acquiring global crop production information

Since NASA's program of research concerning remote sensing was initiated in the 1960s, one of its major objectives has been to advance the state-of-the-art in machine processing of satellite acquired multispectral data. Possibilities have been studied regarding a use of these data to identify type, to monitor condition, and to estimate the ontogenetic stage of cultural vegetation. The present investigation provides a review of the state-of-the-art of the technology used to make remote sensing crop production estimates in foreign regions. Attention is given to Landsat data acquisition, aspects of registration and preprocessing, questions of data transformation, data modeling, proportion estimation, labeling, development stage models, crop condition models, and an outlook regarding future developments.

Hall, F. G.↗
NASA NTRS1984

Space America's commercial space program

Space America prepared a private sector land observing space system which includes a sensor system with eight spectral channels configured for stereoscopic data acquisition of four stereo pairs, a spacecraft bus with active three-axis stabilization, a ground station for data acquisition, preprocessing and retransmission. The land observing system is a component of Space America's end-to-end system for Earth resources management, monitoring and exploration. In the context of the Federal Government's program of commercialization of the US land remote sensing program, Space America's space system is characteristic of US industry's use of advanced technology and of commercial, entrepreneurial management. Well before the issuance of the Request for Proposals for Transfer of the United States Land Remote Sensing Program to the Private Sector by the US Department of Commerce, Space Services, Inc., the managing venturer of Space America, used private funds to develop and manage its sub-orbital launch of its Conestoga launch vehicle.

Macleod, N. H.↗
NASA NTRS1987

Data capture and processing

A systems concept developed in response to the specific requirements imposed by the Space Station and affiliated instrumentation is described. Particular attention is given to those subsystems associated with initial data capture, handling, routing, and distribution control for return link data via the Tracking and Data Relay Satellite System. The conceived approach, designated the Customer Data and Operations System, includes a data interface facility and a data handling center whose functions are data capture, demultiplexing and routing, early preprocessing, and ancillary data handling.

Lyon, John↗
NASA NTRS1990

OMS FDIR: Initial prototyping

The Space Station Freedom Program (SSFP) Operations Management System (OMS) will automate major management functions which coordinate the operations of onboard systems, elements and payloads. The objectives of OMS are to improve safety, reliability and productivity while reducing maintenance and operations cost. This will be accomplished by using advanced automation techniques to automate much of the activity currently performed by the flight crew and ground personnel. OMS requirements have been organized into five task groups: (1) Planning, Execution and Replanning; (2) Data Gathering, Preprocessing and Storage; (3) Testing and Training; (4) Resource Management; and (5) Caution and Warning and Fault Management for onboard subsystems. The scope of this prototyping effort falls within the Fault Management requirements group. The prototyping will be performed in two phases. Phase 1 is the development of an onboard communications network fault detection, isolation, and reconfiguration (FDIR) system. Phase 2 will incorporate global FDIR for onboard systems. Research into the applicability of expert systems, object-oriented programming, fuzzy sets, neural networks and other advanced techniques will be conducted. The goals and technical approach for this new SSFP research project are discussed here.

Taylor, Eric W.↗
NASA NTRS1989

Empirical orthogonal functions and multiple flow regimes in the Southern Hemisphere winter

A new empirical orthogonal function (EOF) analysis of winter 500 mb geopotential height anomalies in the Southern Hemisphere is presented. An earlier EOF analysis prefiltered the anomalies to exclude wavenumbers 5 and higher; the present analysis does not. The different preprocessing of data affects the results. All three distinct planetary flow regimes identified in the winter circulation of the Southern Hemisphere by a pattern correlation method are captured by the new set of EOFs; only two of those regimes were captured by the earlier set. The new results, therefore, lend further support to the idea that EOFs point to distinct planetary flow regimes.

Farrara, John D.↗
NASA NTRS1992

Expert-System Consultant To Operating Personnel

Artificial intelligence aids engineers and technicians in controlling and monitoring complicated systems. Operations Analyst for Distributed Systems (OPERA) software is developmental suite of expert-system computer programs helping engineers and technicians operating from number of computer workstations to control and monitor spacecraft during prelaunch and launch phases of operation. OPERA designed to serve as consultant to operating engineers and technicians. It preprocesses incoming data, using expertise collected from conglomerate of specialists in design and operation of various parts of system. Driven by menus and mouse-activated commands. Modified versions of OPERA used in chemical-processing plants, factories, banks, and other enterprises in which there are distributed-computer systems including computers that monitor or control other computers.

Heard, Astrid E.↗
NASA NTRS2007

Displaying and Analyzing Antenna Radiation Patterns

Radiant Energy Display and Analysis Software Package (REDAP) is a computer program for processing antenna-radiation- pattern data that have been preprocessed by a data-collection program and stored in a spherical-coordinate format. REDAP is designed specifically for application to data generated in testing of antennas in an anechoic chamber at Johnson Space Center; parts of REDAP may be reusable for processing antenna-test data collected elsewhere. REDAP provides a graphical user interface (GUI) and executes mathematical and plotting routines on a personal computer.

Siekierski, Jim↗