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NASA NTRS1992

Using artificial intelligence to control fluid flow computations

Computational simulation is an essential tool for the prediction of fluid flow. Many powerful simulation programs exist today. However, using these programs to reliably analyze fluid flow and other physical situations requires considerable human effort and expertise to set up a simulation, determine whether the output makes sense, and repeatedly run the simulation with different inputs until a satisfactory result is achieved. Automating this process is not only of considerable practical importance but will also significantly advance basic artificial intelligence (AI) research in reasoning about the physical world.

Gelsey, Andrew↗

NASA NTRS1992

Characterization of Artificial Guidestars Generated in the Mesospheric Sodium Layer

Using a 10 W average power sum-frequency laser, we have generated resonant fluorescence beacons in the mesospheric sodium layer and are currently evaluating them for use as an artificial guidestar for atmospheric compensation using adaptive optics. Two flashlamp pumped Nd:YAG lasers operating at 1.064 and 1.319 microns are mixed in a lithium triborate crystal to produce 589 nm light at 840 Hz. The laser emits 47.5 microsec mode-locked pulse trains at 11-14 mJ per pulse and is tuned to the sodium resonance transition with intracavity etalons.

Jelonek, M. P.↗

NASA NTRS1992

A comparison of artificial compressibility and fractional step methods for incompressible flow computations

We have applied and compared the efficiency and accuracy of two commonly used numerical methods for the solution of Navier-Stokes equations. The artificial compressibility method augments the continuity equation with a transient pressure term and allows one to solve the modified equations as a coupled system. Due to its implicit nature, one can have the luxury of taking a large temporal integration step at the expense of higher memory requirement and larger operation counts per step. Meanwhile, the fractional step method splits the Navier-Stokes equations into a sequence of differential operators and integrates them in multiple steps. The memory requirement and operation count per time step are low, however, the restriction on the size of time marching step is more severe. To explore the strengths and weaknesses of these two methods, we used them for the computation of a two-dimensional driven cavity flow with Reynolds number of 100 and 1000, respectively. Three grid sizes, 41 x 41, 81 x 81, and 161 x 161 were used. The computations were considered after the L2-norm of the change of the dependent variables in two consecutive time steps has fallen below 10(exp -5).

Chan, Daniel C.↗

NASA NTRS1991

Human factors issues in the use of artificial intelligence in air traffic control. October 1990 Workshop

The objective of the workshop was to explore the role of human factors in facilitating the introduction of artificial intelligence (AI) to advanced air traffic control (ATC) automation concepts. AI is an umbrella term which is continually expanding to cover a variety of techniques where machines are performing actions taken based upon dynamic, external stimuli. AI methods can be implemented using more traditional programming languages such as LISP or PROLOG, or they can be implemented using state-of-the-art techniques such as object-oriented programming, neural nets (hardware or software), and knowledge based expert systems. As this technology advances and as increasingly powerful computing platforms become available, the use of AI to enhance ATC systems can be realized. Substantial efforts along these lines are already being undertaken at the FAA Technical Center, NASA Ames Research Center, academic institutions, industry, and elsewhere. Although it is clear that the technology is ripe for bringing computer automation to ATC systems, the proper scope and role of automation are not at all apparent. The major concern is how to combine human controllers with computer technology. A wide spectrum of options exists, ranging from using automation only to provide extra tools to augment decision making by human controllers to turning over moment-by-moment control to automated systems and using humans as supervisors and system managers. Across this spectrum, it is now obvious that the difficulties that occur when tying human and automated systems together must be resolved so that automation can be introduced safely and effectively. The focus of the workshop was to further explore the role of injecting AI into ATC systems and to identify the human factors that need to be considered for successful application of the technology to present and future ATC systems.

Hockaday, Stephen↗

NASA NTRS1992

Life sciences and space research XXIV(4) - Natural and artificial ecosystems; Proceedings of the Topical Meeting of the Interdisciplinary Scientific Commission F (Meetings F10, F11, F1 and F12) of the COSPAR 28th Plenary Meeting, The Hague, Netherlands, June 25-July 6, 1990

The present conference on natural and artificial ecosystems and their application to space research encompasses both in-flight and ground-based issues of recycling and control in regenerative life support, the relationships of productivity and facility design in higher plant growth, life-support systems for manned missions to Mars, and biochemical engineering applications in space. Specific issues addressed include interface problems between material recycling systems and plants, temperature and humidity control on a lunar base, the CELSS Test-Facility Project, achieving closure in plant-growth facilities, and life-support systems for Mars transit. Also addressed are a closed equilibrated biological aquatic system, a simulated Mars outpost in the Antarctica dry valleys, analyses of human kidney-cell populations separated on the space shuttle, and the evolution of a phase-separated gravity-independent bioreactor.

Macelroy, R. D.↗

NASA NTRS1990

i-SAIRAS '90; Proceedings of the International Symposium on Artificial Intelligence, Robotics and Automation in Space, Kobe, Japan, Nov. 18-20, 1990

The present conference on artificial intelligence (AI), robotics, and automation in space encompasses robot systems, lunar and planetary robots, advanced processing, expert systems, knowledge bases, issues of operation and management, manipulator control, and on-orbit service. Specific issues addressed include fundamental research in AI at NASA, the FTS dexterous telerobot, a target-capture experiment by a free-flying robot, the NASA Planetary Rover Program, the Katydid system for compiling KEE applications to Ada, and speech recognition for robots. Also addressed are a knowledge base for real-time diagnosis, a pilot-in-the-loop simulation of an orbital docking maneuver, intelligent perturbation algorithms for space scheduling optimization, a fuzzy control method for a space manipulator system, hyperredundant manipulator applications, robotic servicing of EOS instruments, and a summary of astronaut inputs on automation and robotics for the Space Station Freedom.

Source record↗

NASA NTRS1991

The effect of trace additions of Zn on the precipitation behavior of alloy 8090 during artificial aging

The effect(s) of trace additions of Zn to the artificial aging behavior of alloy 8090 (Al-Li-Cu-Mg-Zr) was investigated in the approximate composition range 0-1 wt-pct Zn. Trace Zn additions were found to delay aging and under equivalent aging treatments (100 hrs at 160 C) the alloy without Zn and the 1.07 wt-pct Zn alloy developed delta-prime-free zones along subgrain boundaries, while the alloys of 0.21 and 0.58 wt-pct Zn did not. DSC analysis indicated that Zn was being incorporated into the delta-prime, shifting it's exotherm to higher temperatures, while having little if any effect on its associated endotherm making it unlikely that it is an artifact of a solvus shift. In the 8090 + 1.07 wt-pct Zn alloy, coarse precipitates were found to reside on subgrain boundaries and EDS indicated that they were rich in Cu and Zn. It was also noted that in the Zn containing 8090 varients, the S prime precipitates were more coarse in size than the baseline 8090.

Kilmer, R. J.↗

NASA NTRS1992

Identification of time-varying structural dynamic systems - An artificial intelligence approach

An application of the artificial intelligence-derived methodologies of heuristic search and object-oriented programming to the problem of identifying the form of the model and the associated parameters of a time-varying structural dynamic system is presented in this paper. Possible model variations due to changes in boundary conditions or configurations of a structure are organized into a taxonomy of models, and a variant of best-first search is used to identify the model whose simulated response best matches that of the current physical structure. Simulated model responses are verified experimentally. An output-error approach is used in a discontinuous model space, and an equation-error approach is used in the parameter space. The advantages of the AI methods used, compared with conventional programming techniques for implementing knowledge structuring and inheritance, are discussed. Convergence conditions and example problems have been discussed. In the example problem, both the time-varying model and its new parameters have been identified when changes occur.

Glass, B. J.↗

NASA NTRS1991

Progress in the development of an artificial gravity simulator (AGS)

The paper describes the short-arm centrifuge for studying the effects of acceleration on human subjects which was built at the Artificial Gravity Simulator (AGS) laboratory at Woodlands, Texas. The AGS centrifuge comprises a turntable, a traction system, a platform, four beds, and a data-communication system. Schematic diagrams are presented of the AGS laboratory layout, the centrifuge turntable, the tiltable (up to 6 deg) bed frame, and of the data collection multiplexer (master switch) for physiological data communications.

Cardus, David↗

NASA NTRS1991

Supervised pixel classification using a feature space derived from an artificial visual system

Image segmentation involves labelling pixels according to their membership in image regions. This requires the understanding of what a region is. Using supervised pixel classification, the paper investigates how groups of pixels labelled manually according to perceived image semantics map onto the feature space created by an Artificial Visual System. Multiscale structure of regions are investigated and it is shown that pixels form clusters based on their geometric roles in the image intensity function, not by image semantics. A tentative abstract definition of a 'region' is proposed based on this behavior.

Baxter, Lisa C.↗

NASA NTRS1992

A multilayer perceptron solution to the match phase problem in rule-based artificial intelligence systems

In rule-based AI planning, expert, and learning systems, it is often the case that the left-hand-sides of the rules must be repeatedly compared to the contents of some 'working memory'. The traditional approach to solve such a 'match phase problem' for production systems is to use the Rete Match Algorithm. Here, a new technique using a multilayer perceptron, a particular artificial neural network model, is presented to solve the match phase problem for rule-based AI systems. A syntax for premise formulas (i.e., the left-hand-sides of the rules) is defined, and working memory is specified. From this, it is shown how to construct a multilayer perceptron that finds all of the rules which can be executed for the current situation in working memory. The complexity of the constructed multilayer perceptron is derived in terms of the maximum number of nodes and the required number of layers. A method for reducing the number of layers to at most three is also presented.

Sartori, Michael A.↗

NASA NTRS1991

Artificial intelligence and expert systems in-flight software testing

The authors discuss the introduction of advanced information systems technologies such as artificial intelligence, expert systems, and advanced human-computer interfaces directly into Space Shuttle software engineering. The reconfiguration automation project (RAP) was initiated to coordinate this move towards 1990s software technology. The idea behind RAP is to automate several phases of the flight software testing procedure and to introduce AI and ES into space shuttle flight software testing. In the first phase of RAP, conventional tools to automate regression testing have already been developed or acquired. There are currently three tools in use.

Demasie, M. P.↗

NASA NTRS1992

Space Communication Artificial Intelligence for Link Evaluation Terminal (SCAILET)

A software application to assist end-users of the high burst rate (HBR) link evaluation terminal (LET) for satellite communications is being developed. The HBR LET system developed at NASA Lewis Research Center is an element of the Advanced Communications Technology Satellite (ACTS) Project. The HBR LET is divided into seven major subsystems, each with its own expert. Programming scripts, test procedures defined by design engineers, set up the HBR LET system. These programming scripts are cryptic, hard to maintain and require a steep learning curve. These scripts were developed by the system engineers who will not be available for the end-users of the system. To increase end-user productivity a friendly interface needs to be added to the system. One possible solution is to provide the user with adequate documentation to perform the needed tasks. With the complexity of this system the vast amount of documentation needed would be overwhelming and the information would be hard to retrieve. With limited resources, maintenance is another reason for not using this form of documentation. An advanced form of interaction is being explored using current computer techniques. This application, which incorporates a combination of multimedia and artificial intelligence (AI) techniques to provided end-users with an intelligent interface to the HBR LET system, is comprised of an intelligent assistant, intelligent tutoring, and hypermedia documentation. The intelligent assistant and tutoring systems address the critical programming needs of the end-user.

Shahidi, Anoosh K.↗

NASA NTRS1992

Resource constrained design of artificial neural networks using comparator neural network

We present a systematic design method executed under resource constraints for automating the design of artificial neural networks using the back error propagation algorithm. Our system aims at finding the best possible configuration for solving the given application with proper tradeoff between the training time and the network complexity. The design of such a system is hampered by three related problems. First, there are infinitely many possible network configurations, each may take an exceedingly long time to train; hence, it is impossible to enumerate and train all of them to completion within fixed time, space, and resource constraints. Second, expert knowledge on predicting good network configurations is heuristic in nature and is application dependent, rendering it difficult to characterize fully in the design process. A learning procedure that refines this knowledge based on examples on training neural networks for various applications is, therefore, essential. Third, the objective of the network to be designed is ill-defined, as it is based on a subjective tradeoff between the training time and the network cost. A design process that proposes alternate configurations under different cost-performance tradeoff is important. We have developed a Design System which schedules the available time, divided into quanta, for testing alternative network configurations. Its goal is to select/generate and test alternative network configurations in each quantum, and find the best network when time is expended. Since time is limited, a dynamic schedule that determines the network configuration to be tested in each quantum is developed. The schedule is based on relative comparison of predicted training times of alternative network configurations using comparator network paradigm. The comparator network has been trained to compare training times for a large variety of traces of TSSE-versus-time collected during back-propagation learning of various applications.

Wah, Benjamin W.↗

NASA NTRS1992

Functional expansion representations of artificial neural networks

In the past few years, significant interest has developed in using artificial neural networks to model and control nonlinear dynamical systems. While there exists many proposed schemes for accomplishing this and a wealth of supporting empirical results, most approaches to date tend to be ad hoc in nature and rely mainly on heuristic justifications. The purpose of this project was to further develop some analytical tools for representing nonlinear discrete-time input-output systems, which when applied to neural networks would give insight on architecture selection, pruning strategies, and learning algorithms. A long term goal is to determine in what sense, if any, a neural network can be used as a universal approximator for nonliner input-output maps with memory (i.e., realized by a dynamical system). This property is well known for the case of static or memoryless input-output maps. The general architecture under consideration in this project was a single-input, single-output recurrent feedforward network.

Gray, W. Steven↗

NASA NTRS1992

Lunar subsurface architecture enhanced by artificial biosphere concepts

The integration of artificial biosphere technology with subselene architecture can create a life-enhancing, productive habitat that is safe from solar radiation and extreme temperature fluctuations while maximizing resources brought from Earth and derived from lunar regolith. In the short term, the resulting biotectural (biosphere and architectural) designs will not only make the structures more habitable, productive, and manageable, but will ultimately provide the self-sufficiency factors necessary for the mature lunar settlement. From a long-term perspective, this biotecture approach to astronautics and extraterrestrial development (1) helps reduce mass lift requirements, (2) contributes to habitat self-sufficiency, and (3) actualizes at least one philosophy of solar system exploration, which is to exploit nonterrestrial resources in an effort to conserve our natural resources on this planet.

Klassi, Jason D.↗

NASA NTRS1992

An artificially generated atmosphere near a lunar base

We discuss the formation of an artificial atmosphere generated by vigorous lunar base activity in this paper. We developed an analytical, steady-state model for a lunar atmosphere based upon previous investigations of the Moon's atmosphere from Apollo. Constant gas-injection rates, ballistic trajectories, and a Maxwellian particle distribution for an oxygen-like gas are assumed. Even for the extreme case of continuous He-3 mining of the lunar regolith, we find that the lunar atmosphere would not significantly degrade astronomical observations beyond about 10 km from the mining operation.

Burns, Jack O.↗

NASA NTRS1993

Digital Image Compression Using Artificial Neural Networks

The problem of storing, transmitting, and manipulating digital images is considered. Because of the file sizes involved, large amounts of digitized image information are becoming common in modern projects. Our goal is to described an image compression transform coder based on artificial neural networks techniques (NNCTC). A comparison of the compression results obtained from digital astronomical images by the NNCTC and the method used in the compression of the digitized sky survey from the Space Telescope Science Institute based on the H-transform is performed in order to assess the reliability of the NNCTC.

Serra-Ricart, M.↗