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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 NTRS2000

Iterative Repair Planning for Spacecraft Operations Using the Aspen System

This paper describes the Automated Scheduling and Planning Environment (ASPEN). ASPEN encodes complex spacecraft knowledge of operability constraints, flight rules, spacecraft hardware, science experiments and operations procedures to allow for automated generation of low level spacecraft sequences. Using a technique called iterative repair, ASPEN classifies constraint violations (i.e., conflicts) and attempts to repair each by performing a planning or scheduling operation. It must reason about which conflict to resolve first and what repair method to try for the given conflict. ASPEN is currently being utilized in the development of automated planner/scheduler systems for several spacecraft, including the UFO-1 naval communications satellite and the Citizen Explorer (CX1) satellite, as well as for planetary rover operations and antenna ground systems automation. This paper focuses on the algorithm and search strategies employed by ASPEN to resolve spacecraft operations constraints, as well as the data structures for representing these constraints.

Rabideau, G.↗

NASA NTRS1999

Iterative Repair Planning for Spacecraft Operations Using the Aspen System

planning↗

NASA NTRS2001

Terrestrial Environmental Variables Derived From EOS Platform Sensors

The three main objectives of the overall project were: 1. Adaptation of environmental constraint methods to take advantage of EOS sensors, specifically, MODIS, ASTER, and Landsat-7, in addition to the PM AVHRR observations 2. Refinement of environmental constraint methods based on fundamental scientific knowledge. 3. Assessment of spatial scaling patterns in environmental constraint measurements to evaluate the potential biases and errors that occur when estimating regional and global-scale NPP patterns with moderate to coarse satellite observations. These goals were modified because, on one hand, MODIS data did not become available until after the first year of the project and because of project staffing issues at the University of Maryland., The OSU portion of the project contained a modest amount of funding and responsibility compared to the University of Maryland and the University of Toledo.

Stadler, Stephen J.↗

NASA NTRS1991

Dynamic edge warping - An experimental system for recovering disparity maps in weakly constrained systems

Dynamic edge warping (DEW), a technique for recovering reasonably accurate disparity maps from uncalibrated stereo image pairs, is presented. No precise knowledge of the epipolar camera geometry is assumed. The technique is embedded in a system including structural stereopsis on the front end and robust estimation in digital photogrammetry on the other for the purpose of self-calibrating stereo image pairs. Once the relative camera orientation is known, the epipolar geometry is computed and the system can use this information to refine its representation of the object space. Such a system will find application in the autonomous extraction of terrain maps from stereo aerial photographs, for which camera position and orientation are unknown a priori, and for online autonomous calibration maintenance for robotic vision applications, in which the cameras are subject to vibration and other physical disturbances after calibration. This work thus forms a component of an intelligent system that begins with a pair of images and, having only vague knowledge of the conditions under which they were acquired, produces an accurate, dense, relative depth map. The resulting disparity map can also be used directly in some high-level applications involving qualitative scene analysis, spatial reasoning, and perceptual organization of the object space. The system as a whole substitutes high-level information and constraints for precise geometric knowledge in driving and constraining the early correspondence process.

Boyer, K. L.↗

NASA NTRS1992

Reformulating Constraints for Compilability and Efficiency

KBSDE is a knowledge compiler that uses a classification-based approach to map solution constraints in a task specification onto particular search algorithm components that will be responsible for satisfying those constraints (e.g., local constraints are incorporated in generators; global constraints are incorporated in either testers or hillclimbing patchers). Associated with each type of search algorithm component is a subcompiler that specializes in mapping constraints into components of that type. Each of these subcompilers in turn uses a classification-based approach, matching a constraint passed to it against one of several schemas, and applying a compilation technique associated with that schema. While much progress has occurred in our research since we first laid out our classification-based approach [Ton91], we focus in this paper on our reformulation research. Two important reformulation issues that arise out of the choice of a schema-based approach are: (1) compilability-- Can a constraint that does not directly match any of a particular subcompiler's schemas be reformulated into one that does? and (2) Efficiency-- If the efficiency of the compiled search algorithm depends on the compiler's performance, and the compiler's performance depends on the form in which the constraint was expressed, can we find forms for constraints which compile better, or reformulate constraints whose forms can be recognized as ones that compile poorly? In this paper, we describe a set of techniques we are developing for partially addressing these issues.

Tong, Chris↗

NASA NTRS1990

Carbon chemistry in dense molecular clouds: Theory and observational constraints

For the most part, gas phase models of the chemistry of dense molecular clouds predict the abundances of simple species rather well. However, for larger molecules and even for small systems rich in carbon these models often fail spectacularly. Researchers present a brief review of the basic assumptions and results of large scale modeling of the carbon chemistry in dense molecular clouds. Particular attention is to the influence of the gas phase C/O ratio in molecular clouds, and the likely role grains play in maintaining this ratio as clouds evolve from initially diffuse objects to denser cores with associated stellar and planetary formation. Recent spectral line surveys at centimeter and millimeter wavelengths along with selected observations in the submillimeter have now produced an accurate inventory of the gas phase carbon budget in several different types of molecular clouds, though gaps in our knowledge clearly remain. The constraints these observations place on theoretical models of interstellar chemistry can be used to gain insights into why the models fail, and show also which neglected processes must be included in more complete analyses. Looking toward the future, larger molecules are especially difficult to study both experimentally and theoretically in such dense, cold regions, and some new methods are therefore outlined which may ultimately push the detectability of small carbon chains and rings to much heavier species.

Blake, Geoffrey A.↗

NASA NTRS2022

After DART: Using the first full-scale test of a kinetic impactor to inform a future planetary defense mission

NASA’s Double Asteroid Redirection Test (DART) is the first full-scale test of an asteroid deflection technology. Results from the hypervelocity kinetic impact and Earth-based observations, coupled with LICIACube and the later Hera mission, will result in measurement of the momentum transfer efficiency accurate to ~10% and characterization of the Didymos binary system. But DART is a single experiment; how could these results be used in a future planetary defense necessity involving a different asteroid? We examine what aspects of Dimorphos’s response to kinetic impact will be constrained by DART results; how these constraints will help refine knowledge of the physical properties of asteroidal materials and predictive power of impact simulations; what information about a potential Earth impactor could be acquired before a deflection effort; and how design of a deflection mission should be informed by this understanding. We generalize the momentum enhancement factor β, showing that a particular direction-specific β will be directly determined by the DART results, and that a related direction-specific β is a figure of merit for a kinetic impact mission. The DART β determination constrains the ejecta momentum vector, which, with hydrodynamic simulations, constrains the physical properties of Dimorphos’s near-surface. In a hypothetical planetary defense exigency, extrapolating these constraints to a newly discovered asteroid will require Earth-based observations and benefit from in-situ reconnaissance. We show representative predictions for momentum transfer based on different levels of reconnaissance and discuss strategic targeting to optimize the deflection and reduce the risk of a counterproductive deflection in the wrong direction.

Asteroids↗

NASA NTRS1988

Knowledge based tools for Hubble Space Telescope planning and scheduling: Constraints and strategies

The Hubble Space Telescope (HST) presents an especially challenging scheduling problem since a year's observing program encompasses tens of thousands of exposures facing numerous coupled constraints. Recent progress in the development of planning and scheduling tools is discussed which augment the existing HST ground system. General methods for representing activities, constraints, and constraint satisfaction, and time segmentation were implemented in a scheduling testbed. The testbed permits planners to evaluate optimal scheduling time intervals, calculate resource usage, and to generate long and medium range plans. Graphical displays of activities, constraints, and plans are an important feature of the system. High-level scheduling strategies using rule based and neural net approaches were implemented.

Miller, Glenn↗

NASA NTRS1990

Adaptive pattern recognition by mini-max neural networks as a part of an intelligent processor

In this decade and progressing into 21st Century, NASA will have missions including Space Station and the Earth related Planet Sciences. To support these missions, a high degree of sophistication in machine automation and an increasing amount of data processing throughput rate are necessary. Meeting these challenges requires intelligent machines, designed to support the necessary automations in a remote space and hazardous environment. There are two approaches to designing these intelligent machines. One of these is the knowledge-based expert system approach, namely AI. The other is a non-rule approach based on parallel and distributed computing for adaptive fault-tolerances, namely Neural or Natural Intelligence (NI). The union of AI and NI is the solution to the problem stated above. The NI segment of this unit extracts features automatically by applying Cauchy simulated annealing to a mini-max cost energy function. The feature discovered by NI can then be passed to the AI system for future processing, and vice versa. This passing increases reliability, for AI can follow the NI formulated algorithm exactly, and can provide the context knowledge base as the constraints of neurocomputing. The mini-max cost function that solves the unknown feature can furthermore give us a top-down architectural design of neural networks by means of Taylor series expansion of the cost function. A typical mini-max cost function consists of the sample variance of each class in the numerator, and separation of the center of each class in the denominator. Thus, when the total cost energy is minimized, the conflicting goals of intraclass clustering and interclass segregation are achieved simultaneously.

Szu, Harold H.↗

NASA NTRS1990

Influence of structural and aerodynamic modeling on optimization with flutter constraint

The influences of structural and aerodynamic modeling on multidisciplinary optimization in an aeroelastic environment are not well understood. Therefore, optimizations with flutter and frequency constraints were performed to investigate the effects these modeling factors have on various representative wings. To this end, the Automated Structural Optimization System (ASTROS) was used as a tool to minimize the weight of various fully built-up finite element wing models in subsonic and supersonic flow under given flutter and frequency constraints. First, the performance of the optimization module was tested against results from other codes on a straight and uniform wing widely used for optimization with flutter constraints. Then, fully built-up finite element models of various wings with different aspect ratios were investigated for the influence on the structural optimization for minimum weight of the following modeling factors: finite element selection, structural grid refinement; number of selected modes, retention of breathing modes; selection of reduced frequencies to be used in flutter analysis; aerodynamic panel size and placement; splining of the aerodynamic grid to the structural grid selection of extra points of the structural wing box for splining; and number of constraints to be retained. Knowledge of these influences as well as of the program behavior is important, since optimization can be made more efficient by the selection of reasonable initial models. Also, it was shown previously that modeling has an impact on the results of modal and aeroelastic analyses. Thus, if modeling errors can negatively affect the analyses, a minimum weight optimization can be jeopardized and result in an optimal design that is rather unreliable. In the following, selected results are presented and the influences of some modeling parameters on optimization are pointed out.

Striz, Alfred G.↗

NASA NTRS2002

Data Assimilation and Transport Modeling in Terrestrial and Planetary Atmospheres

Data assimilation is a blanket term used to describe a number of techniques for retrieving important physical parameters from observational data, subject to constraints imposed by prior knowledge (such as, in the case of meteorology, the primitive equations that govern atmospheric motion). Since these newly developed methods make efficient use of computational resources, they are of great importance in the interpretation of the voluminous datasets that are now produced by satellite missions. As proposed, these techniques have been applied to the study of the Martian and terrestrial atmospheres based on available satellite observations. In addition, a sophisticated hydrodynamic model (non-hydrostatic, and therefore applicable to the study of the interiors of the giant planets) has also been developed and successfully applied to the study of tidally induced motions in Jupiter.

Houben, Howard C.↗

NASA NTRS2017

3DHZETRN: Inhomogeneous Geometry Issues

Historical methods for assessing radiation exposure inside complicated geometries for space applications were limited by computational constraints and lack of knowledge associated with nuclear processes occurring over a broad range of particles and energies. Various methods were developed and utilized to simplify geometric representations and enable coupling with simplified but efficient particle transport codes. Recent transport code development efforts, leading to 3DHZETRN, now enable such approximate methods to be carefully assessed to determine if past exposure analyses and validation efforts based on those approximate methods need to be revisited. In this work, historical methods of representing inhomogeneous spacecraft geometry for radiation protection analysis are first reviewed. Two inhomogeneous geometry cases, previously studied with 3DHZETRN and Monte Carlo codes, are considered with various levels of geometric approximation. Fluence, dose, and dose equivalent values are computed in all cases and compared. It is found that although these historical geometry approximations can induce large errors in neutron fluences up to 100 MeV, errors on dose and dose equivalent are modest (<10%) for the cases studied here.

Wilson, John W.↗

NASA NTRS1989

Reasoning about software specifications - A case study

The launch intercept condition (LIC) problem was analyzed to better understand the complexity of the automated reasoning process and the nature and role of background knowledge. An extension to resolution-based logic processing systems, called constraint logic programming, is described. Consideration is also given to a knowledge-driven incremental reasoning architecture which integrates a generic constraint logic programming system with a knowledge base of the domain.

Wild, Chris↗

NASA NTRS1977

The masses of neutron stars - Observational constraints

The present state of empirical knowledge concerning neutron-star masses is reviewed on the basis of information obtained for the nine known X-ray pulsars in binary systems. Pulse profiles and other pulse parameters of these X-ray pulsars are examined, and allowable mass ranges are estimated for four X-ray pulsars in binary systems as well as for the binary pulsar PSR 1913+16. The approximate mass ranges obtained include 1.2 to 2.4 solar masses for 3U 0900-40, 0.7 to 4.3 solar masses for Cen X-3, 0.8 to 1.8 solar masses for SMC X-1, 0.0 to 2.3 solar masses for Her X-1, and either 1.4 to 1.8 solar masses or 0.0 to 1.8 solar masses for PSR 1913+16 if the companion is a normal white dwarf or some other type of star, respectively. Theoretical implications of these results are briefly considered.

Rappaport, S. A.↗

NASA NTRS1991

Theoretical foundations for information representation and constraint specification

Research accomplished at the Knowledge Based Systems Laboratory of the Department of Industrial Engineering at Texas A&M University is described. Outlined here are the theoretical foundations necessary to construct a Neutral Information Representation Scheme (NIRS), which will allow for automated data transfer and translation between model languages, procedural programming languages, database languages, transaction and process languages, and knowledge representation and reasoning control languages for information system specification.

Menzel, Christopher P.↗

NASA NTRS1987

AC-67 Press Conference

On March 26, 1987, after the launch of an Atlas/Centaur rocket with a payload of a Navy Communications Satellite, a problem developed and the rocket was lost. This videotape is a press conference held to review the incident. Mr. John Gibb, the Atlas-Centaur Program Manager at Lewis Research Center, opens the press conference with a statement that reviews the situation, and what is known about the accident. He reviews the constraints to launch and explains that to the best of his knowledge there was no violation of these constraints. He further states that a review panel will investigate the circumstances and make recommendations. The press conference is then opened up to questions. Most of the questions concern the weather conditions and the existence of lightning in the area. The Air Force representative, Colonel John Albrook, is asked if the loss of the satellite would pose any problems. He answers that there were several satellites performing the role for which this satellite was slated, and that these were still healthy, and capable of continuing for a considerable length of time.

Source record↗

NASA NTRS1993

Knowledge engineering for temporal dependency networks as operations procedures

This paper presents a case study of the knowledge engineering process employed to support the Link Monitor and Control Operator Assistant (LMCOA). The LMCOA is a prototype system which automates the configuration, calibration, test, and operation (referred to as precalibration) of the communications, data processing, metric data, antenna, and other equipment used to support space-ground communications with deep space spacecraft in NASA's Deep Space Network (DSN). The primary knowledge base in the LMCOA is the Temporal Dependency Network (TDN), a directed graph which provides a procedural representation of the precalibration operation. The TDN incorporates precedence, temporal, and state constraints and uses several supporting knowledge bases and data bases. The paper provides a brief background on the DSN, and describes the evolution of the TDN and supporting knowledge bases, the process used for knowledge engineering, and an analysis of the successes and problems of the knowledge engineering effort.

Fayyad, Kristina E.↗