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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
NASA NTRS1986

Soil types and forest canopy structures in southern Missouri: A first look with AIS data

Spectral reflectance properties of deciduous oak-hickory forests covering the eastern half of the Rolla Quadrangle were examined using Thematic Mapper (TM) data acquired in August and December, 1982 and Airborne Imaging Spectrometer (AIS) data acquired in August, 1985. For the TM data distinctly high relative reflectance values (greater than 0.3) in the near infrared (Band 4, 0.73 to 0.94 micrometers) correspond to regions characterized by xeric (dry) forests that overlie soils with low water retention capacities. These soils are derived primarily from rhyolites. More mesic forests characterized by lower TM band 4 relative reflectances are associated with soils of higher retention capacities derived predominately from non-cherty carbonates. The major factors affecting canopy reflectance appear to be the leaf area index (LAI) and leaf optical properties. The Suits canopy reflectance model predicts the relative reflectance values for the xeric canopies. The mesic canopy reflectance is less well matched and incorporation of canopy shadowing caused by the irregular nature of the mesic canopy may be necessary. Preliminary examination of high spectral resolution AIS data acquired in August of 1985 reveals no more information than found in the broad band TM data.

Green, G. M.↗
NASA NTRS1986

AIS spectra of desert shrub canopies

Airborne Imaging Spectrometer (AIS) data were collected 30 August 1985 from a desert shrub community in central Oregon. Spectra from artificial targets placed on the test site and from bare soil, big sagebrush (Artemesia tridentata wyomingensis), silver sagebrush (Artemesia cana bolander), and exposed volcanic rocks were studied. Spectral data from grating position 3 (tree mode) were selected from 25 ground positions for analysis by Principal Factor Analysis (PFA). In this grating position, as many as six factors were identified as significant in contributing to spectral structure. Channels 74 through 84 (tree mode) best characterized between-class differences. Other channels were identified as nondiscriminating and as associated with such errors as excessive atmospheric absorption and grating positin changes. The test site was relatively simple with the two species (A. tridentata and A. cana) representing nearly 95% of biomass and with only two mineral backgrounds, a montmorillonitic soil and volcanic rocks. If, as in this study, six factors of spectral structure can be extracted from a single grating position from data acquired over a simple vegetation community, then AIS data must be considered rich in information-gathering potential.

Murray, R.↗
NASA NTRS1985

AI at Ames: Artificial Intelligence research and application at NASA Ames Research Center, Moffett Field, California, February 1985

Charts are given that illustrate function versus domain for artificial intelligence (AI) applications and interests and research area versus project number for AI research. A list is given of project titles with associated project numbers and page numbers. Also, project descriptions, including title, participants, and status are given.

Andrews, Alison E.↗
NASA NTRS1986

AI challenges for spacecraft control programs

The application of AI technology to the spacecraft and experiment command and control systems environment is proposed. The disadvantages of the present methods for analyzing and resolving spacecraft experiment command and control problems are discussed. The potential capabilities and advantages of using AI for the spacecraft and experiment command and control systems are described.

Lightfoot, Patricia↗
NASA NTRS1987

Overview of Austrian Airborne Imaging Spectrometer (AIS) programme and first results

Airborne Imaging Spectrometer (AIS) data collected from eight test areas in Austria were evaluated for their usefulness in forest damage assessment, geobotany, alpine vegetation mapping, and land use classification. Difficulties encountered in installing the SPAM spectral analysis software for use on the image display system and the necessity to adapt existing programs for this task impeded and delayed the analysis of the AIS data. Spectral reflectance curves obtained from a geobotanical test site show a marked increase in reflectance across most of the measured spectrum for metal stressed spruce trees compared with nonstressed spruce trees.

Banninger, C.↗
NASA NTRS1987

Preliminary results from an investigation of AIS-1 data over an area of epithermal alteration: Plateau, Northern Queensland, Australia

Airborne Imaging Spectrometer-1 (AIS-1) data were flown over undifferentiated sequences of acid to intermediate volcanics and intrusives; meta-sediments; and a series of partially lateritized sedimentary rocks. The area exhibits a considerable spectral variability, after the suppression of striping effects. Log residual, and Internal Average Relative Reflectance (IARR) analytical techniques were used to enhance mineralogically related spectral features. Both methods produce similar results, but did not visually highlight mineral absorption features due to processing artifacts in areas of significant vegetation cover. The enhancement of mineral related absorption features was achieved using a hybrid processing approach based on the relative reflectance differences between vegetated and non-vegetated surfaces at 1.2 and 2.1 micron. The result is an image with little overall contrast, but which enhances the more subtle spectral features believed to be associated with clays and epidote. The AIS data was subject to interactive analysis using SPAM. Clear separation of clay and epidote related absorption features was apparent, and the identification of kaolinite was possible despite detrimental spectral effects.

Mackin, Steve↗
NASA NTRS1987

Rapid prototyping and AI programming environments applied to payload modeling

This effort focused on using artificial intelligence (AI) programming environments and rapid prototyping to aid in both space flight manned and unmanned payload simulation and training. Significant problems addressed are the large amount of development time required to design and implement just one of these payload simulations and the relative inflexibility of the resulting model to accepting future modification. Results of this effort have suggested that both rapid prototyping and AI programming environments can significantly reduce development time and cost when applied to the domain of payload modeling for crew training. The techniques employed are applicable to a variety of domains where models or simulations are required.

Carnahan, Richard S., Jr.↗
NASA NTRS1988

AIS radiometry and the problem of contamination from mixed spectral orders

The spectral radiance of test areas under solar illumination is ascertained in view of Airborne Imaging Spectrometer (AIS) data from Mono Lake, CA, establishing an atmospheric correction method for major absorbers on the basis of the spectrometric data themselves. The apparent low contrast of all atmospheric absorption bands leads to a study of contamination from overlapping spectral orders in the AIS data; this contamination is found unambiguously above 1500 nm with a magnitude that is a factor of 1.5-2.0 greater than the expected uncontaminated signal alone.

Conel, J. E.↗
NASA NTRS1989

Diverter AI based decision aid, phases 1 and 2

It was determined that a system to incorporate artificial intelligence (AI) into airborne flight management computers is feasible. The AI functions that would be most useful to the pilot are to perform situational assessment, evaluate outside influences on the contemplated rerouting, perform flight planning/replanning, and perform maneuver planning. A study of the software architecture and software tools capable of demonstrating Diverter was also made. A skeletal planner known as the Knowledge Acquisition Development Tool (KADET), which is a combination script-based and rule-based system, was used to implement the system. A prototype system was developed which demonstrates advanced in-flight planning/replanning capabilities.

Sexton, George A.↗
NASA NTRS1988

Planning and scheduling in AI

An account is given of the many related activities employing AI that are classifiable as 'automatic planning and scheduling'. A human can form plans and successfully execute them, but no current automatic-planning AI system can match this robustness and generality; it is in fact suggested that automatic planning is unlikely to be achieved by a general-purpose planning system. It is judged likely that partially-specialized planners will emerge for the efficient solution of specific classes of problems. Current planners are also found to make unrealistic informational demands, especially in the requirement that the 'state of the world' be known at all times, and that the only changes that occur are under the planner's control.

Cheeseman, Peter↗
NASA NTRS1989

Application of Artificial Intelligence (AI) programming techniques to tactical guidance for fighter aircraft

A research program investigating the use of Artificial Intelligence (AI) programming techniques to aid in the development of a Tactical Decision Generator (TDG) for Within-Visual-Range (WVR) air combat engagements is discussed. The application of AI methods for development and implementation of the TDG is presented. The history of the Adaptive Maneuvering Logic (AML) program is traced and current versions of the (AML) program is traced and current versions of the AML program are compared and contrasted with the TDG system. The Knowledge-Based Systems (KBS) used by the TDG to aid in the decision-making process are outlined and example rules are presented. The results of tests to evaluate the performance of the TDG against a version of AML and against human pilots in the Langley Differential Maneuvering Simulator (DMS) are presented. To date, these results have shown significant performance gains in one-versus-one air combat engagements.

Mcmanus, John W.↗
NASA NTRS1988

AI, automation and the Flight Telerobotic Servicer

A NASA study for the preliminary definition of a teleoperated robotic device has been recently completed. The Fligt Telerobotic Servicer (FTS) will be used to assist astronauts in many of the on-board tasks of assembly, maintenance, servicing, and inspection of the Space Station. The role of artificial intelligence (AI) in furthering the FTS automation capabilities and, hence, extending its capacity for growth and evolution is discussed. Relevant system engineering issues are identified, and an approach for insertion of AI technology is presented in terms of the NASA/NBS Standard Reference Model control architecture NASREM.

Goforth, Andre↗
NASA NTRS1991

AI techniques for a space application scheduling problem

Scheduling is a very complex optimization problem which can be categorized as an NP-complete problem. NP-complete problems are quite diverse, as are the algorithms used in searching for an optimal solution. In most cases, the best solutions that can be derived for these combinatorial explosive problems are near-optimal solutions. Due to the complexity of the scheduling problem, artificial intelligence (AI) can aid in solving these types of problems. Some of the factors are examined which make space application scheduling problems difficult and presents a fairly new AI-based technique called tabu search as applied to a real scheduling application. the specific problem is concerned with scheduling application. The specific problem is concerned with scheduling solar and stellar observations for the SOLar-STellar Irradiance Comparison Experiment (SOLSTICE) instrument in a constrained environment which produces minimum impact on the other instruments and maximizes target observation times. The SOLSTICE instrument will gly on-board the Upper Atmosphere Research Satellite (UARS) in 1991, and a similar instrument will fly on the earth observing system (Eos).

Thalman, N.↗
NASA NTRS1991

AI mass spectrometers for space shuttle health monitoring

The facility Hazardous Gas Detection System (HGDS) at Kennedy Space Center (KSC) is a mass spectrometer based gas analyzer. Two instruments make up the HGDS, which is installed in a prime/backup arrangement, with the option of using both analyzers on the same sample line, or on two different lines simultaneously. It is used for monitoring the Shuttle during fuel loading, countdown, and drainback, if necessary. The use of complex instruments, operated over many shifts, has caused problems in tracking the status of the ground support equipment (GSE) and the vehicle. A requirement for overall system reliability has been a major force in the development of Shuttle GSE, and is the ultimate driver in the choice to pursue artificial intelligence (AI) techniques for Shuttle and Advanced Launch System (ALS) mass spectrometer systems. Shuttle applications of AI are detailed.

Adams, F. W.↗
NASA NTRS1990

Launching AI in NASA ground systems

This paper will discuss recent operational successes in implementing expert systems to support the complex functions of NASA mission control systems at the Goddard Space Flight Center, including fault detection and diagnosis for real time and engineering analysis functions in the Cosmic Background Explorer and Gamma Ray Observatory missions and automation of resource planning and scheduling functions for various missions. It will also discuss ongoing developments and prototypes that will lead to increasingly sophisticated applications of artificial intelligence. These include the use of neural networks to perform telemetry monitoring functions, the implementation of generic expert system shells that can be customized to telemetry handling functions specific to NASA control centers, the applications of AI in training and user support, the long-term potential of implementing systems based around distributed, cooperative problem solving, and the use of AI to control and assist system development activities.

Perkins, Dorothy C.↗
NASA NTRS1991

AI aerospace components

An evaluation is made of the application of novel, AI-capabilities-related technologies to aerospace systems. Attention is given to expert-system shells for Space Shuttle Orbiter mission control, manpower and processing cost reductions at the NASA Kennedy Space Center's 'firing rooms' for liftoff monitoring, the automation of planetary exploration systems such as semiautonomous mobile robots, and AI for battlefield staff-related functions.

Heindel, Troy A.↗
NASA NTRS1990

AI applications to conceptual aircraft design

This paper presents in viewgraph form several applications of artificial intelligence (AI) to the conceptual design of aircraft, including: an access manager for automated data management, AI techniques applied to optimization, and virtual reality for scientific visualization of the design prototype.

Kathryn M. Chalfan↗
NASA NTRS1993

Mission activities planning for a Hermes mission by means of AI-technology

Mission Activities Planning is a complex task to be performed by mission control centers. AI technology can offer attractive solutions to the planning problem. This paper presents the use of a new AI-based Mission Planning System for crew activity planning. Based on a HERMES servicing mission to the COLUMBUS Man Tended Free Flyer (MTFF) with complex time and resource constraints, approximately 2000 activities with 50 different resources have been generated, processed, and planned with parametric variation of operationally sensitive parameters. The architecture, as well as the performance of the mission planning system, is discussed. An outlook to future planning scenarios, the requirements, and how a system like MARS can fulfill those requirements is given.

Pape, U.↗