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NASA NTRS2000

Center for Advanced Computational Technology

The Center for Advanced Computational Technology (ACT) was established to serve as a focal point for diverse research activities pertaining to application of advanced computational technology to future aerospace systems. These activities include the use of numerical simulations, artificial intelligence methods, multimedia and synthetic environments, and computational intelligence, in the modeling, analysis, sensitivity studies, optimization, design and operation of future aerospace systems. The Center is located at NASA Langley and is an integral part of the School of Engineering and Applied Science of the University of Virginia. The Center has four specific objectives: 1) conduct innovative research on applications of advanced computational technology to aerospace systems; 2) act as pathfinder by demonstrating to the research community what can be done (high-potential, high-risk research); 3) help in identifying future directions of research in support of the aeronautical and space missions of the twenty-first century; and 4) help in the rapid transfer of research results to industry and in broadening awareness among researchers and engineers of the state-of-the-art in applications of advanced computational technology to the analysis, design prototyping and operations of aerospace and other high-performance engineering systems. In addition to research, Center activities include helping in the planning and coordination of the activities of a multi-center team of NASA and JPL researchers who are developing an intelligent synthesis environment for future aerospace systems; organizing workshops and national symposia; as well as writing state-of-the-art monographs and NASA special publications on timely topics.

Noor, Ahmed K.↗

NASA NTRS2000

ISS Microgravity Environment Monitoring System (MEMS): System Design - Part 1

The Principal Investigator Microgravity Services project at the NASA Glenn Research center supports Principal Investigators of the microgravity science community as they evaluate the effects of acceleration on their experiments. The Principal Investigator Microgravity Services' primary responsibility is to support NASA sponsored investigators in the area of acceleration data analysis, interpretation and the monitoring of the microgravity environment on-board various carriers. The microgravity environment is a rich and very complex dynamic one. It is subject to quasi-steady accelerations, higher frequency acceleration, and transient disturbances. With the advent of the International Space Station operation, a significant amount of data is expected to be downlinked and processed for both the space station microgravity environment characterization (verification) and scientific experiments. Therefore, to help principal investigator teams monitor the microgravity environment on-board the International Space Station in order to avoid negative impact on their experiment, when possible, the NASA Glenn Principal Investigator Microgravity Services project is currently developing an artificial intelligence monitoring system, which will notify the principal investigator teams in near real time of any change in the microgravity environment susceptible to affect their experiments.

Jules, Kenol↗

NASA NTRS2001

Monitoring the Microgravity Environment Quality On-Board the International Space Station Using Soft Computing Techniques

This paper presents an artificial intelligence monitoring system developed by the NASA Glenn Principal Investigator Microgravity Services project to help the principal investigator teams identify the primary vibratory disturbance sources that are active, at any moment in time, on-board the International Space Station, which might impact the microgravity environment their experiments are exposed to. From the Principal Investigator Microgravity Services' web site, the principal investigator teams can monitor via a graphical display, in near real time, which event(s) is/are on, such as crew activities, pumps, fans, centrifuges, compressor, crew exercise, platform structural modes, etc., and decide whether or not to run their experiments based on the acceleration environment associated with a specific event. This monitoring system is focused primarily on detecting the vibratory disturbance sources, but could be used as well to detect some of the transient disturbance sources, depending on the events duration. The system has built-in capability to detect both known and unknown vibratory disturbance sources. Several soft computing techniques such as Kohonen's Self-Organizing Feature Map, Learning Vector Quantization, Back-Propagation Neural Networks, and Fuzzy Logic were used to design the system.

Jules, Kenol↗

NASA NTRS1994

A Knowledge-Based Representation Scheme for Environmental Science Models

One of the primary methods available for studying environmental phenomena is the construction and analysis of computational models. We have been studying how artificial intelligence techniques can be applied to assist in the development and use of environmental science models within the context of NASA-sponsored activities. We have identified several high-utility areas as potential targets for research and development: model development; data visualization, analysis, and interpretation; model publishing and reuse, training and education; and framing, posing, and answering questions. Central to progress on any of the above areas is a representation for environmental models that contains a great deal more information than is present in a traditional software implementation. In particular, a traditional software implementation is devoid of any semantic information that connects the code with the environmental context that forms the background for the modeling activity. Before we can build AI systems to assist in model development and usage, we must develop a representation for environmental models that adequately describes a model's semantics and explicitly represents the relationship between the code and the modeling task at hand. We have developed one such representation in conjunction with our work on the SIGMA (Scientists' Intelligent Graphical Modeling Assistant) environment. The key feature of the representation is that it provides a semantic grounding for the symbols in a set of modeling equations by linking those symbols to an explicit representation of the underlying environmental scenario.

Keller, Richard M.↗

NASA NTRS1994

Operations on Graphical Models with Plates

This paper explains how graphical models, for instance Bayesian or Markov networks, can be extended to model problems in data analysis and learning. This provides a unified framework that combines lessons learned from the artificial intelligence, statistical and connectionist communities. This also offers a set of principles for developing a software generator for data analysis, whereby a learning or discovery system can be compiled from specifications. Many of the popular learning algorithms can be compiled in this way from graphical specifications. While in a sense this paper is a multidisciplinary review of learning, the main contribution here is the presentation of the material within the unifying framework of graphical models, and the observation that, as a result, the process of developing learning algorithms can be partly automated.

Buntine, Wray L.↗

NASA NTRS1994

A Guide to the Literature on Learning Graphical Models

This literature review discusses different methods under the general rubric of learning Bayesian networks from data, and more generally, learning probabilistic graphical models. Because many problems in artificial intelligence, statistics and neural networks can be represented as a probabilistic graphical model, this area provides a unifying perspective on learning. This paper organizes the research in this area along methodological lines of increasing complexity.

Buntine, Wray L.↗

NASA NTRS2001

Case-Based Capture and Reuse of Aerospace Design Rationale

The goal of this project was to apply artificial intelligence techniques to facilitate capture and reuse of aerospace design rationale. The project combined case-based reasoning (CBR) and concept maps (CMaps) to develop methods for capturing, organizing, and interactively accessing records of experiences encapsulating the methods and rationale underlying expert aerospace design, in order to bring the captured knowledge to bear to support future reasoning. The project's results contribute both principles and methods for effective design-aiding systems that aid capture and access of useful design knowledge. The project has been guided by the tenets that design-aiding systems must: (1) Leverage a designer's knowledge, rather than attempting to replace it; (2) Be able to reflect different designers' differing conceptualizations of the design task, and to clarify those conceptualizations to others; (3) Include capabilities to capture information both by interactive knowledge modeling and during normal use; and (4) Integrate into normal designer tasks as naturally and unobtrusive as possible.

Leake, David B.↗

NASA NTRS1994

A Spacelab Expert System for Remote Engineering and Science

NASA's space science program is based on strictly pre-planned activities. This approach does not always result in the best science. We describe an existing computer system that enables space science to be conducted in a more reactive manner through advanced automation techniques that have recently been used in SLS-2 October 1993 space shuttle flight. Advanced computing techniques, usually developed in the field of Artificial Intelligence, allow large portions of the scientific investigator's knowledge to be "packaged" in a portable computer to present advice to the astronaut operator. We strongly believe that this technology has wide applicability to other forms of remote science/engineering. In this brief article, we present the technology of remote science/engineering assistance as implemented for the SLS-2 space shuttle flight. We begin with a logical overview of the system (paying particular attention to the implementation details relevant to the use of the embedded knowledge for system reasoning), then describe its use and success in space, and conclude with ideas about possible earth uses of the technology in the life and medical sciences.

Groleau, Nick↗

NASA NTRS1994

The NASA Ames Closed Environmental Research Chamber: Present Status

The Closed Environmental Research Chamber (CERC) at the NASA Ames Research Center was created to investigate both components and complete systems for life support of advanced space exploration missions. This facility includes a Main Chamber, an Airlock, a Sample Transfer Lock, a Vacuum System, an Air Recompression System, a dedicated control room and a pit area for housing supporting and environmental control systems. The Main Chamber provides 310 sq ft of internal working/living space on two levels. It is planned that the CERC will be a human-rated facility for habitation simulation under mass balance closure conditions. The internal pressure will be variable over the range of 14.7 psia to 5 psia with accompanying capability for variation in atmosphere composition to maintain the oxygen partial pressure at 160 mm Hg. The CERC will be provided with a core set of primary life support subsystems for temperature and humidity control, C02 removal and trace contaminant control. Interfacing with external life support technology test b~ds with be provided, along with connection to centralized, microprocessor-based data acquisition and control systems. This paper will discuss the current status of the CERC facility and show how it is being used to address the advanced technology requirements necessary to implement an integrated working and living environment for a planetary habitat. In particular, it will be shown how the CERC, along with a human-powered centrifuge, a planetary terrain simulator and advanced displays and a virtual reality capability will work together to develop and demonstration applicable technologies for future planetary habitats. Artificial intelligence and expert system programming techniques will be used extensively to provide an automated environment for a 4-person crew. There will be several robotic mechanisms performing exploration tasks external to the habitat that will be controlled through the virtual environment to provide representative workloads for the crew. Finally, there will be a discussion of how effective are innovative new multidisciplinary test facilities to the investigation of the wide range of human and machine problems inherent in exploration missions.

Gross, Anthony R.↗

NASA NTRS2002

Can Tectonic and Fluvial Structures be Used to Date a Planetary Surface?

For the first part of our investigation, we will use a new artificial intelligent (AI) software code designed to: (1) recognize craters down to two pixel resolution in size and (2) calculate crater statistics on all available Viking imagery. Additional information is contained in the original extended abstract.

Vinogradova, T.↗

NASA NTRS2002

The Pixon Method for Data Compression Image Classification, and Image Reconstruction

As initially proposed, this program had three goals: (1) continue to develop the highly successful Pixon method for image reconstruction and support other scientist in implementing this technique for their applications; (2) develop image compression techniques based on the Pixon method; and (3) develop artificial intelligence algorithms for image classification based on the Pixon approach for simplifying neural networks. Subsequent to proposal review the scope of the program was greatly reduced and it was decided to investigate the ability of the Pixon method to provide superior restorations of images compressed with standard image compression schemes, specifically JPEG-compressed images.

Puetter, Richard↗

NASA NTRS2000

Giving E-Commerce a Boost

A former Ames employee, Monte Zweben, founded a new company, Blue Martini Software, that provides software to companies seeking to personalize their products to individual customers. This customer targeting approach is accomplished through the use of artificial intelligence concepts Zweben worked on while at Ames. The Ames AI research has found applications in clickstream mining and purchasing behavior data collection.

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NASA NTRS2001

UNIPASS for AvSP? A Broader View

UNIPASS is a general-purpose probabilistic computer program consisting of three major modules, including preprocessor, solver and postprocessor. UNIPASS contains a user-friendly Graphical User Interface (GUI), numerous state-of-the-art probabilistic analysis techniques, a large library of statistical distributions and a function module with a large library of support functions that can easily define any complex limit-state function in a scripting FORTRAN-like syntax format. Its inverse probability analysis and sensitivities analysis capabilities make it a powerful design aid in any product cycle. Its precise numerical analysis engine is accurate enough to push the failure probabilities of a design to well below 10 (exp -50). UNIPASS is equipped with advanced artificial intelligence that is designed to handle systems with an essentially unlimited number of random variables with ease and efficiency. Its modular arrangement allows you to tailor an analysis to the desired level of accuracy and efficiency. The depth and comprehensiveness of UNIPASS are built upon the decades of experience and expertise of industry leaders including Boeing Aircraft, NASA and the DoD. Its rich content also makes UNIPASS a valuable instructional tool for random processes and probabilistic mechanics. The topics include: 1) Reliability in AvSP; 2) Role of UNIPASS in AvSP; and 3) Examples. This paper is in viewgraph form.

Wu, N. Eva↗

NASA NTRS1994

Blowout Monitor

C Language Integrated Production System (CLIPS), a NASA-developed software shell for developing expert systems, has been embedded in a PC-based expert system for training oil rig personnel in monitoring oil drilling. Oil drilling rigs if not properly maintained for possible blowouts pose hazards to human life, property and the environment may be destroyed. CLIPS is designed to permit the delivery of artificial intelligence on computer. A collection of rules is set up and, as facts become known, these rules are applied. In the Well Site Advisor, CLIPS provides the capability to accurately process, predict and interpret well data in a real time mode. CLIPS was provided to INTEQ by COSMIC.

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NASA NTRS2002

A System for Planning Ahead

A software system that uses artificial intelligence techniques to help with complex Space Shuttle scheduling at Kennedy Space Center is commercially available. Stottler Henke Associates, Inc.(SHAI), is marketing its automatic scheduling system, the Automated Manifest Planner (AMP), to industries that must plan and project changes many different times before the tasks are executed. The system creates optimal schedules while reducing manpower costs. Using information entered into the system by expert planners, the system automatically makes scheduling decisions based upon resource limitations and other constraints. It provides a constraint authoring system for adding other constraints to the scheduling process as needed. AMP is adaptable to assist with a variety of complex scheduling problems in manufacturing, transportation, business, architecture, and construction. AMP can benefit vehicle assembly plants, batch processing plants, semiconductor manufacturing, printing and textiles, surface and underground mining operations, and maintenance shops. For most of SHAI's commercial sales, the company obtains a service contract to customize AMP to a specific domain and then issues the customer a user license.

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NASA NTRS1993

Seminar Software

The Society for Computer Simulation International is a professional technical society that distributes information on methodology techniques and uses of computer simulation. The society uses NETS, a NASA-developed program, to assist seminar participants in learning to use neural networks for computer simulation. NETS is a software system modeled after the human brain; it is designed to help scientists exploring artificial intelligence to solve pattern matching problems. Examples from NETS are presented to seminar participants, who can then manipulate, alter or enhance them for their own applications.

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NASA NTRS1999

Aerospace Engineering Systems

Continuous improvement of aerospace product development processes is a driving requirement across much of the aerospace community. As up to 90% of the cost of an aerospace product is committed during the first 10% of the development cycle, there is a strong emphasis on capturing, creating, and communicating better information (both requirements and performance) early in the product development process. The community has responded by pursuing the development of computer-based systems designed to enhance the decision-making capabilities of product development individuals and teams. Recently, the historical foci on sharing the geometrical representation and on configuration management are being augmented: Physics-based analysis tools for filling the design space database; Distributed computational resources to reduce response time and cost; Web-based technologies to relieve machine-dependence; and Artificial intelligence technologies to accelerate processes and reduce process variability. Activities such as the Advanced Design Technologies Testbed (ADTT) project at NASA Ames Research Center study the strengths and weaknesses of the technologies supporting each of these trends, as well as the overall impact of the combination of these trends on a product development event. Lessons learned and recommendations for future activities will be reported.

VanDalsem, William R.↗

NASA NTRS1992

Small Business Innovations (Integrated Database)

Because of the diversity of NASA's information systems, it was necessary to develop DAVID as a central database management system. Under a Small Business Innovation Research (SBIR) grant, Ken Wanderman and Associates, Inc. designed software tools enabling scientists to interface with DAVID and commercial database management systems, as well as artificial intelligence programs. The software has been installed at a number of data centers and is commercially available.

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