SEARCH · Engineering Papers

Results for “artificial”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 469 records · Page 26

All sources NASA NTRS DOE OSTI

NASA NTRS1990

Fundamental research in artificial intelligence at NASA

This paper describes basic research at NASA in the field of artificial intelligence. The work is conducted at the Ames Research Center and the Jet Propulsion Laboratory, primarily under the auspices of the NASA-wide Artificial Intelligence Program in the Office of Aeronautics, Exploration and Technology. The research is aimed at solving long-term NASA problems in missions operations, spacecraft autonomy, preservation of corporate knowledge about NASA missions and vehicles, and management/analysis of scientific and engineering data. From a scientific point of view, the research is broken into the categories of: planning and scheduling; machine learning; and design of and reasoning about large-scale physical systems.

Friedland, Peter↗

NASA NTRS1992

A comparison of polynomial approximations and artificial neural nets as response surfaces

Artificial neural nets and polynomial approximations were used to develop response surfaces for several test problems. Based on the number of functional evaluations required to build the approximations and the number of undetermined parameters associated with the approximations, the performance of the two types of approximations was found to be comparable. A rule of thumb is developed for determining the number of nodes to be used on a hidden layer of an artificial neural net, and the number of designs needed to train an approximation is discussed.

Carpenter, William C.↗

NASA NTRS1992

Surface heat transfer and flow properties of vortex arrays induced artificially and from centrifugal instabilities

The paper presents and compares fluid-flow and heat transfer properties from artificially induced vortices in a flat-plate turbulent boundary layer and naturally occurring vortices due to centrifugal instabilities in a curved-channel laminar flow. Pairs and arrays of vortices are artificially induced by placing half-delta wings on the plate surface. With both arrays and pairs of vortices, streamwise velocities and total pressures are high, and surface heat transfer is locally augmented in vortex downwash regions. In contrast to vortices in the arrays vortices in the pairs tend to move in the streamwise direction with significant divergence (when the common flow between pair is toward the wall) or convergence (when the common flow between pair is away from the wall). The vortices in the arrays cause maximum peak-to-peak heat transfer variations of up to 12 percent of local spanwise-averaged values for initial vortex spacings between 1 to 2.5 generator heights.

Subramanian, C. S.↗

NASA NTRS1993

Generalized Adaptive Artificial Neural Networks

Mathematical model of supervised learning by artificial neural network provides for simultaneous adjustments of both temperatures of neurons and synaptic weights, and includes feedback as well as feedforward synaptic connections. Extension of mathematical model described in "Adaptive Neurons For Artificial Neural Networks" (NPO-17803). Dynamics of neural network represented in new model by less-restrictive continuous formalism.

Tawel, Raoul↗

NASA NTRS1993

The 1993 Goddard Conference on Space Applications of Artificial Intelligence

This publication comprises the papers presented at the 1993 Goddard Conference on Space Applications of Artificial Intelligence held at the NASA/Goddard Space Flight Center, Greenbelt, MD on May 10-13, 1993. The purpose of this annual conference is to provide a forum in which current research and development directed at space applications of artificial intelligence can be presented and discussed.

Hostetter, Carl F.↗

NASA NTRS1993

Data fusion with artificial neural networks (ANN) for classification of earth surface from microwave satellite measurements

A data fusion system with artificial neural networks (ANN) is used for fast and accurate classification of five earth surface conditions and surface changes, based on seven SSMI multichannel microwave satellite measurements. The measurements include brightness temperatures at 19, 22, 37, and 85 GHz at both H and V polarizations (only V at 22 GHz). The seven channel measurements are processed through a convolution computation such that all measurements are located at same grid. Five surface classes including non-scattering surface, precipitation over land, over ocean, snow, and desert are identified from ground-truth observations. The system processes sensory data in three consecutive phases: (1) pre-processing to extract feature vectors and enhance separability among detected classes; (2) preliminary classification of Earth surface patterns using two separate and parallely acting classifiers: back-propagation neural network and binary decision tree classifiers; and (3) data fusion of results from preliminary classifiers to obtain the optimal performance in overall classification. Both the binary decision tree classifier and the fusion processing centers are implemented by neural network architectures. The fusion system configuration is a hierarchical neural network architecture, in which each functional neural net will handle different processing phases in a pipelined fashion. There is a total of around 13,500 samples for this analysis, of which 4 percent are used as the training set and 96 percent as the testing set. After training, this classification system is able to bring up the detection accuracy to 94 percent compared with 88 percent for back-propagation artificial neural networks and 80 percent for binary decision tree classifiers. The neural network data fusion classification is currently under progress to be integrated in an image processing system at NOAA and to be implemented in a prototype of a massively parallel and dynamically reconfigurable Modular Neural Ring (MNR).

Lure, Y. M. Fleming↗

NASA NTRS1993

Calibration of a shock wave position sensor using artificial neural networks

This report discusses the calibration of a shock wave position sensor. The position sensor works by using artificial neural networks to map cropped CCD frames of the shadows of the shock wave into the value of the shock wave position. This project was done as a tutorial demonstration of method and feasibility. It used a laboratory shadowgraph, nozzle, and commercial neural network package. The results were quite good, indicating that artificial neural networks can be used efficiently to automate the semi-quantitative applications of flow visualization.

Decker, Arthur J.↗

NASA NTRS1993

Artificial auroras in the upper atmosphere. I - Electron beam injections

The Atlas-1 Spacelab payload's Space Experiments with Particle Accelerators generated artificial electron beams for the stimulation of auroral emissions at southern auroral latitudes. Optical measurements were made by the Shuttle Orbiter's onboard TV cameras, as well as by the Atmospheric Emissions Photometric Imager (in both white light and the 427.8 nm N2(+) emission line). Shuttle-based auroral imaging furnished a novel perspective on the artificial auroras; the emissions were traced from 295 km to the 110 km level along the curved magnetic-field lines.

Burch, J. L.↗

NASA NTRS1920

Tests of artificial flight at high altitudes

If we wish to form an accurate idea of the extraordinary progress achieved in aeronautics, a comparison must be made of the latest altitude records and the figures regarded as highest attainable limit some ten years ago. It is desirable, for two reasons, that we should be able to define the limit of the altitudes that can be reached without artificial aid. First, to know to what extent the human body can endure the inhalation of rarified air. Second, the mental capacity of the aviator must be tested at high altitudes and the limit known below which he is able to make reliable observations without being artificially supplied with oxygen. A pneumatic chamber was used for the most accurate observations.

Gradenwitz, Arthur↗

NASA NTRS1952

Some Features of Artificially Thickened Fully Developed Turbulent Boundary Layers with Zero Pressure Gradient

Report gives an account of an investigation conducted to determine the feasibility of artificially thickening a turbulent boundary layer on a flat plate. A description is given of several methods used to thicken artificially the boundary layer. It is shown that it is possible to do substantial thickening and obtain a fully developed turbulent boundary layer, which is free from any distortions introduced by the thickening process, and, as such, is a suitable medium for fundamental research.

Klebanoff, P S↗

NASA NTRS1993

Functional approximation using artificial neural networks in structural mechanics

The artificial neural networks (ANN) methodology is an outgrowth of research in artificial intelligence. In this study, the feed-forward network model that was proposed by Rumelhart, Hinton, and Williams was applied to the mapping of functions that are encountered in structural mechanics problems. Several different network configurations were chosen to train the available data for problems in materials characterization and structural analysis of plates and shells. By using the recall process, the accuracy of these trained networks was assessed.

Alam, Javed↗

NASA NTRS1993

Apparatus and method for producing an artificial gravitational field

An apparatus and method is disclosed for producing an artificial gravitational field in a spacecraft by rotating the same around a spin axis. The centrifugal force thereby created acts as an artificial gravitational force. The apparatus includes an engine which produces a drive force offset from the spin axis to drive the spacecraft towards a destination. The engine is also used as a counterbalance for a crew cabin for rotation of the spacecraft. Mass of the spacecraft, which may include either the engine or crew cabin, is shifted such that the centrifugal force acting on that mass is no longer directed through the center of mass of the craft. This off-center centrifugal force creates a moment that counterbalances the moment produced by the off-center drive force to eliminate unwanted rotation which would otherwise be precipitated by the offset drive force.

Mccanna, Jason↗

NASA NTRS1993

Generation of artificial helioseismic time-series

We present an outline of an algorithm to generate artificial helioseismic time-series, taking into account as much as possible of the knowledge we have on solar oscillations. The hope is that it will be possible to find the causes of some of the systematic errors in analysis algorithms by testing them with such artificial time-series.

Schou, J.↗

NASA NTRS1994

Artificial plasma experiments. Chemical release observations associated with the CRRES program

This report submitted is the final report and covers work performed under the contract for the period Apr. 12, 1985 - Dec. 23, 1993. The CRRES program investigated earth plasma environment by active experiments in which metal vapors were injected into the upper atmosphere and magnetosphere. The vapor clouds perturb the ambient ionospheric / magnetospheric environment and the effects could be monitored by passive observing instruments. Our part of the CRRES program, the Artificial Plasma Experiment program, was a ground based and aircraft based investigation to observe artificial chemical releases by optical techniques.

Mende, Stephen B.↗

NASA NTRS1993

Development of programmable artificial neural networks

Conventionally programmed digital computers can process numbers with great speed and precision, but do not easily recognize patterns or imprecise or contradictory data. Instead of being programmed in the conventional sense, artificial neural networks are capable of self-learning through exposure to repeated examples. However, the training of an ANN can be a time consuming and unpredictable process. A general method is being developed to mate the adaptability of the ANN with the speed and precision of the digital computer. This method was successful in building feedforward networks that can approximate functions and their partial derivatives from examples in a single iteration. The general method also allows the formation of feedforward networks that can approximate the solution to nonlinear ordinary and partial differential equations to desired accuracy without the need of examples. It is believed that continued research will produce artificial neural networks that can be used with confidence in practical scientific computing and engineering applications.

Meade, Andrew J.↗

NASA NTRS1992

Artificial Intelligence Research Branch future plans

This report contains information on the activities of the Artificial Intelligence Research Branch (FIA) at NASA Ames Research Center (ARC) in 1992, as well as planned work in 1993. These activities span a range from basic scientific research through engineering development to fielded NASA applications, particularly those applications that are enabled by basic research carried out in FIA. Work is conducted in-house and through collaborative partners in academia and industry. All of our work has research themes with a dual commitment to technical excellence and applicability to NASA short, medium, and long-term problems. FIA acts as the Agency's lead organization for research aspects of artificial intelligence, working closely with a second research laboratory at the Jet Propulsion Laboratory (JPL) and AI applications groups throughout all NASA centers. This report is organized along three major research themes: (1) Planning and Scheduling: deciding on a sequence of actions to achieve a set of complex goals and determining when to execute those actions and how to allocate resources to carry them out; (2) Machine Learning: techniques for forming theories about natural and man-made phenomena; and for improving the problem-solving performance of computational systems over time; and (3) Research on the acquisition, representation, and utilization of knowledge in support of diagnosis design of engineered systems and analysis of actual systems.

Stewart, Helen↗

NASA NTRS1994

The 1994 Goddard Conference on Space Applications of Artificial Intelligence

This publication comprises the papers presented at the 1994 Goddard Conference on Space Applications of Artificial Intelligence held at the NASA/GSFC, Greenbelt, Maryland, on 10-12 May 1994. The purpose of this annual conference is to provide a forum in which current research and development directed at space applications of artificial intelligence can be presented and discussed.

Hostetter, Carl F.↗

NASA NTRS1994

Third International Symposium on Artificial Intelligence, Robotics, and Automation for Space 1994

The Third International Symposium on Artificial Intelligence, Robotics, and Automation for Space (i-SAIRAS 94), held October 18-20, 1994, in Pasadena, California, was jointly sponsored by NASA, ESA, and Japan's National Space Development Agency, and was hosted by the Jet Propulsion Laboratory (JPL) of the California Institute of Technology. i-SAIRAS 94 featured presentations covering a variety of technical and programmatic topics, ranging from underlying basic technology to specific applications of artificial intelligence and robotics to space missions. i-SAIRAS 94 featured a special workshop on planning and scheduling and provided scientists, engineers, and managers with the opportunity to exchange theoretical ideas, practical results, and program plans in such areas as space mission control, space vehicle processing, data analysis, autonomous spacecraft, space robots and rovers, satellite servicing, and intelligent instruments.

Source record↗