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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.
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.
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.
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.
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.
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.
This publication comprises the papers presented at the 1995 Goddard Conference on Space Applications of Artificial Intelligence and Emerging Information Technologies held at the NASA/Goddard Space Flight Center, Greenbelt, Maryland, on May 9-11, 1995. 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.
We present a study of the effect of artificial dissipation models on nonlinear wave computations using a few high order schemes. Our motivation is to assess the effectiveness of artificial dissipation models for their suitability for aeroacoustic computations. We solve three model problems in one dimension using the Euler equations. Initial conditions are chosen to generate nonlinear waves in the computational domain. We examine various dissipation models in central difference schemes such as the Dispersion Relation Preserving (DRP) scheme and the standard fourth and sixth order schemes. We also make a similar study with the fourth order MacCormack scheme due to Gottieb and Turkel.
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 (ANN's) 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 by the author to mate the adaptability of the ANN with the speed and precision of the digital computer. This method has been 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.
Nutrients contained in sandlike material. Artificial soil provides nutrients to plants during several growing seasons without need to add fertilizer or nutrient solution. When watered, artificial soil slowly releases all materials a plant needs to grow. Developed as medium for growing crops in space. Also used to grow plants on Earth under controlled conditions or even to augment natural soil.
The motion of an artificial satellite about its center of mass is studied considering torques due to the gravity gradient and direct solar radiation pressure. A model for direct solar radiation torque is derived for a circular cylindrical satellite. An analytical solution is obtained by the method of variation of the parameters. This solution shows that the angular variables have secular variation but that the modulus of the rotational angular momentum, the projection of rotational angular momentum on the z axis of the moment of inertia and inertial axis z, suffer only periodic variations. Considering a hypothetical artificial satellite, a numerical application is demonstrated.
This paper describes an expert system which is designed to perform automatic data analysis, identify anomalous events, and determine the characteristic features of these events. We have employed both artificial intelligence and neural net approaches in the design of this expert system. The artificial intelligence approach is useful because it provides (1) the use of human experts' knowledge of sensor behavior and faulty engine conditions in interpreting data; (2) the use of engine design knowledge and physical sensor locations in establishing relationships among the events of multiple sensors; (3) the use of stored analysis of past data of faulty engine conditions; and (4) the use of knowledge-based reasoning in distinguishing sensor failure from actual faults. The neural network approach appears promising because neural nets (1) can be trained on extremely noisy data and produce classifications which are more robust under noisy conditions than other classification techniques; (2) avoid the necessity of noise removal by digital filtering and therefore avoid the need to make assumptions about frequency bands or other signal characteristics of anomalous behavior; (3) can, in effect, generate their own feature detectors based on the characteristics of the sensor data used in training; and (4) are inherently parallel and therefore are potentially implementable in special-purpose parallel hardware.
In this paper, we propose a new technique for the numerical treatment of external flow problems with oscillatory behavior of the solution in time. Specifically, we consider the case of unbounded compressible viscous plane flow past a finite body (airfoil). Oscillations of the flow in time may be caused by the time-periodic injection of fluid into the boundary layer, which in accordance with experimental data, may essentially increase the performance of the airfoil. To conduct the actual computations, we have to somehow restrict the original unbounded domain, that is, to introduce an artificial (external) boundary and to further consider only a finite computational domain. Consequently, we will need to formulate some artificial boundary conditions (ABC's) at the introduced external boundary. The ABC's we are aiming to obtain must meet a fundamental requirement. One should be able to uniquely complement the solution calculated inside the finite computational domain to its infinite exterior so that the original problem is solved within the desired accuracy. Our construction of such ABC's for oscillating flows is based on an essential assumption: the Navier-Stokes equations can be linearized in the far field against the free-stream back- ground. To actually compute the ABC's, we represent the far-field solution as a Fourier series in time and then apply the Difference Potentials Method (DPM) of V. S. Ryaben'kii. This paper contains a general theoretical description of the algorithm for setting the DPM-based ABC's for time-periodic external flows. Based on our experience in implementing analogous ABC's for steady-state problems (a simpler case), we expect that these boundary conditions will become an effective tool for constructing robust numerical methods to calculate oscillatory flows.
This report presents a theory of oblateness perturbations of the orbits of artificial satellites based on Hansen's theory, with modification for adaptation to fast machine computation. The theory permits the easy inclusion of any gravitational terms and is suitable for the deduction of geo-physical and geodetic data from orbit observations on artificial satellites. The computations can be carried out to any desired order compatible with the accuracy of the geodetic parameters.
An essential item in the development of this project was the availability of the artificial gravity simulator (AGS). At the termination of that grant in 1994, the AGS was dismantled and transferred to NASA Johnson Space Center. It took over two years for the AGS to be re-assembled and re-certified for use. As a consequence of the non-availability of the AGS for two years, there was a considerable delay in implementing the various phases of the project. The subjects involved in the study were eight healthy able bodied subjects and twelve with spinal cord injury. After analysis of the data collected on these subjects, six of the healthy able bodied subjects and three of the sub ects with spinal cord injury were found to qualify for the study. This report gives the results of four subjects only, two healthy able bodied and two spinal cord injured subjects because the period of the grant (1 year) and its extension (1 year) expired before additional subjects could be studied. The principal objective of the study was to conduct a series of experiments to demonstrate the feasibility of utilizing artificial gravity to assist in the physical rehabilitation of persons with spinal cord injuries.
This talk will present the work which has been done at NASA Marshall Space Flight Center involving the use of Artificial Intelligence to control the power system in a spacecraft. The presentation will include a brief history of power system automation, and some basic definitions of the types of artificial intelligence which have been investigated at MSFC for power system automation. A video tape of one of our autonomous power systems using co-operating expert systems, and advanced hardware will be presented.
The extent to which extraterrestrial life questions can be addressed, in the absence of an actual example, rests in some measure on the extent to which terrestrial life is representative of life in general since we will likely have to draw heavily, if not completely, from terrestrial life research. One example of a practical question involving extraterrestrial life that arises in preparing for a human mission to another planet such as Mars, is trying to assess and minimize the possible adverse effects of the presence of humans on possible indigenous extraterrestrial life-forms. This paper will present some key planetary protection challenges for a human Mars mission and then focus on one possible approach for assessing the extent to which terrestrial life is representative of biological phenomena in general, informing perhaps, the level of confidence we might have in applying terrestrial research - to extraterrestrial life issues. The approach involves appealing to the relatively new field of Artificial Life (A-Life) to: (1) use what might be the most basic minimal set of life-defining characteristics in (2) a large number of open-ended Artificial Life simulations to generate a "life possibility space" (3) the products of which can be examined for their plausibility within the context of relevant constraining knowledge, so that (4) the remaining possibility space can be examined for its variability relative to terrestrial life, where low variability might suggest that terrestrial life is representative of life in general, and high variability would indicate otherwise.
We construct global artificial boundary conditions (ABCs) for the numerical simulation of wave processes on unbounded domains using a special non-deteriorating algorithm that has been developed previously for the long-term computation of wave-radiation solutions. The ABCs are obtained directly for the discrete formulation of the problem; in so doing, neither a rational approximation of 'non-reflecting kernels,' nor discretization of the continuous boundary conditions is required. The extent of temporal nonlocality of the new ABCs appears fixed and limited; in addition, the ABCs can handle artificial boundaries of irregular shape on regular grids with no fitting/adaptation needed and no accuracy loss induced. The non-deteriorating algorithm, which is the core of the new ABCs is inherently three-dimensional, it guarantees temporally uniform grid convergence of the solution driven by a continuously operating source on arbitrarily long time intervals, and provides unimprovable linear computational complexity with respect to the grid dimension. The algorithm is based on the presence of lacunae, i.e., aft fronts of the waves, in wave-type solutions in odd-dimension spaces, It can, in fact, be built as a modification on top of any consistent and stable finite-difference scheme, making its grid convergence uniform in time and at the same time keeping the rate of convergence the same as that of the non-modified scheme. In the paper, we delineate the construction of the global lacunae-based ABCs in the framework of a discretized wave equation. The ABCs are obtained for the most general formulation of the problem that involves radiation of waves by moving sources (e.g., radiation of acoustic waves by a maneuvering aircraft). We also present systematic numerical results that corroborate the theoretical design properties of the ABCs' algorithm.