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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.

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At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
NASA NTRS1988

The design of an intelligent human-computer interface for the test, control and monitor system

The graphical intelligence and assistance capabilities of a human-computer interface for the Test, Control, and Monitor System at Kennedy Space Center are explored. The report focuses on how a particular commercial off-the-shelf graphical software package, Data Views, can be used to produce tools that build widgets such as menus, text panels, graphs, icons, windows, and ultimately complete interfaces for monitoring data from an application; controlling an application by providing input data to it; and testing an application by both monitoring and controlling it. A complete set of tools for building interfaces is described in a manual for the TCMS toolkit. Simple tools create primitive widgets such as lines, rectangles and text strings. Intermediate level tools create pictographs from primitive widgets, and connect processes to either text strings or pictographs. Other tools create input objects; Data Views supports output objects directly, thus output objects are not considered. Finally, a set of utilities for executing, monitoring use, editing, and displaying the content of interfaces is included in the toolkit.

Shoaff, William D.↗
NASA NTRS1984

Artificial intelligence, expert systems, computer vision, and natural language processing

An overview of artificial intelligence (AI), its core ingredients, and its applications is presented. The knowledge representation, logic, problem solving approaches, languages, and computers pertaining to AI are examined, and the state of the art in AI is reviewed. The use of AI in expert systems, computer vision, natural language processing, speech recognition and understanding, speech synthesis, problem solving, and planning is examined. Basic AI topics, including automation, search-oriented problem solving, knowledge representation, and computational logic, are discussed.

Gevarter, W. B.↗
NASA NTRS1992

A computer architecture for intelligent machines

The theory of intelligent machines proposes a hierarchical organization for the functions of an autonomous robot based on the principle of increasing precision with decreasing intelligence. An analytic formulation of this theory using information-theoretic measures of uncertainty for each level of the intelligent machine has been developed. The authors present a computer architecture that implements the lower two levels of the intelligent machine. The architecture supports an event-driven programming paradigm that is independent of the underlying computer architecture and operating system. Execution-level controllers for motion and vision systems are briefly addressed, as well as the Petri net transducer software used to implement coordination-level functions. A case study illustrates how this computer architecture integrates real-time and higher-level control of manipulator and vision systems.

Lefebvre, D. R.↗
NASA NTRS1991

A computer architecture for intelligent machines

The Theory of Intelligent Machines proposes a hierarchical organization for the functions of an autonomous robot based on the Principle of Increasing Precision With Decreasing Intelligence. An analytic formulation of this theory using information-theoretic measures of uncertainty for each level of the intelligent machine has been developed in recent years. A computer architecture that implements the lower two levels of the intelligent machine is presented. The architecture supports an event-driven programming paradigm that is independent of the underlying computer architecture and operating system. Details of Execution Level controllers for motion and vision systems are addressed, as well as the Petri net transducer software used to implement Coordination Level functions. Extensions to UNIX and VxWorks operating systems which enable the development of a heterogeneous, distributed application are described. A case study illustrates how this computer architecture integrates real-time and higher-level control of manipulator and vision systems.

Lefebvre, D. R.↗
NASA NTRS1979

Artificial intelligence programming languages for computer aided manufacturing

Eight Artificial Intelligence programming languages (SAIL, LISP, MICROPLANNER, CONNIVER, MLISP, POP-2, AL, and QLISP) are presented and surveyed, with examples of their use in an automated shop environment. Control structures are compared, and distinctive features of each language are highlighted. A simple programming task is used to illustrate programs in SAIL, LISP, MICROPLANNER, and CONNIVER. The report assumes reader knowledge of programming concepts, but not necessarily of the languages surveyed.

Rieger, C.↗
NASA NTRS1984

Computational aerodynamics and artificial intelligence

Some aspects of artificial intelligence are considered and questions are speculated on, including how knowledge-based systems can accelerate the process of acquiring new knowledge in aerodynamics, how computational fluid dynamics may use 'expert' systems and how expert systems may speed the design and development process. The anatomy of an idealized expert system called AERODYNAMICIST is discussed. Resource requirements are examined for using artificial intelligence in computational fluid dynamics and aerodynamics. Considering two of the essentials of computational aerodynamics - reasoniing and calculating - it is believed that a substantial part of the reasoning can be achieved with artificial intelligence, with computers being used as reasoning machines to set the stage for calculating. Expert systems will probably be new assets of institutions involved in aeronautics for various tasks of computational aerodynamics.

Kutler, P.↗
NASA NTRS1984

Computational aerodynamics and artificial intelligence

The general principles of artificial intelligence are reviewed and speculations are made concerning how knowledge based systems can accelerate the process of acquiring new knowledge in aerodynamics, how computational fluid dynamics may use expert systems, and how expert systems may speed the design and development process. In addition, the anatomy of an idealized expert system called AERODYNAMICIST is discussed. Resource requirements for using artificial intelligence in computational fluid dynamics and aerodynamics are examined. Three main conclusions are presented. First, there are two related aspects of computational aerodynamics: reasoning and calculating. Second, a substantial portion of reasoning can be achieved with artificial intelligence. It offers the opportunity of using computers as reasoning machines to set the stage for efficient calculating. Third, expert systems are likely to be new assets of institutions involved in aeronautics for various tasks of computational aerodynamics.

Mehta, U. B.↗
NASA NTRS1988

The development of an intelligent interface to a computational fluid dynamics flow-solver code

Researchers at NASA Lewis are currently developing an 'intelligent' interface to aid in the development and use of large, computational fluid dynamics flow-solver codes for studying the internal fluid behavior of aerospace propulsion systems. This paper discusses the requirements, design, and implementation of an intelligent interface to Proteus, a general purpose, 3-D, Navier-Stokes flow solver. The interface is called PROTAIS to denote its introduction of artificial intelligence (AI) concepts to the Proteus code.

Williams, Anthony D.↗
NASA NTRS1988

The development of an intelligent interface to a computational fluid dynamics flow-solver code

Researchers at NASA Lewis are currently developing an 'intelligent' interface to aid in the development and use of large, computational fluid dynamics flow-solver codes for studying the internal fluid behavior of aerospace propulsion systems. This paper discusses the requirements, design, and implementation of an intelligent interface to Proteus, a general purpose, three-dimensional, Navier-Stokes flow solver. The interface is called PROTAIS to denote its introduction of artificial intelligence (AI) concepts to the Proteus code.

Williams, Anthony D.↗
NASA NTRS2001

Implementation of a General Real-Time Visual Anomaly Detection System Via Soft Computing

The intelligent visual system detects anomalies or defects in real time under normal lighting operating conditions. The application is basically a learning machine that integrates fuzzy logic (FL), artificial neural network (ANN), and generic algorithm (GA) schemes to process the image, run the learning process, and finally detect the anomalies or defects. The system acquires the image, performs segmentation to separate the object being tested from the background, preprocesses the image using fuzzy reasoning, performs the final segmentation using fuzzy reasoning techniques to retrieve regions with potential anomalies or defects, and finally retrieves them using a learning model built via ANN and GA techniques. FL provides a powerful framework for knowledge representation and overcomes uncertainty and vagueness typically found in image analysis. ANN provides learning capabilities, and GA leads to robust learning results. An application prototype currently runs on a regular PC under Windows NT, and preliminary work has been performed to build an embedded version with multiple image processors. The application prototype is being tested at the Kennedy Space Center (KSC), Florida, to visually detect anomalies along slide basket cables utilized by the astronauts to evacuate the NASA Shuttle launch pad in an emergency. The potential applications of this anomaly detection system in an open environment are quite wide. Another current, potentially viable application at NASA is in detecting anomalies of the NASA Space Shuttle Orbiter's radiator panels.

Dominguez, Jesus A.↗
NASA NTRS1998

Computational Nanoelectronics and Nanotechnology at NASA ARC

Both physical and economic considerations indicate that the scaling era of CMOS will run out of steam around the year 2010. However, physical laws also indicate that it is possible to compute at a rate of a billion times present speeds with the expenditure of only one Watt of electrical power. NASA has long-term needs where ultra-small semiconductor devices are needed for critical applications: high performance, low power, compact computers for intelligent autonomous vehicles and Petaflop computing technology are some key examples. To advance the design, development, and production of future generation micro- and nano-devices, IT Modeling and Simulation Group has been started at NASA Ames with a goal to develop an integrated simulation environment that addresses problems related to nanoelectronics and molecular nanotechnology. Overview of nanoelectronics and nanotechnology research activities being carried out at Ames Research Center will be presented. We will also present the vision and the research objectives of the IT Modeling and Simulation Group including the applications of nanoelectronic based devices relevant to NASA missions.

Saini, Subhash↗
NASA NTRS1998

Computational Nanoelectronics and Nanotechnology at NASA ARC

Both physical and economic considerations indicate that the scaling era of CMOS will run out of steam around the year 2010. However, physical laws also indicate that it is possible to compute at a rate of a billion times present speeds with the expenditure of only one Watt of electrical power. NASA has long-term needs where ultra-small semiconductor devices are needed for critical applications: high performance, low power, compact computers for intelligent autonomous vehicles and Petaflop computing technolpgy are some key examples. To advance the design, development, and production of future generation micro- and nano-devices, IT Modeling and Simulation Group has been started at NASA Ames with a goal to develop an integrated simulation environment that addresses problems related to nanoelectronics and molecular nanotecnology. Overview of nanoelectronics and nanotechnology research activities being carried out at Ames Research Center will be presented. We will also present the vision and the research objectives of the IT Modeling and Simulation Group including the applications of nanoelectronic based devices relevant to NASA missions.

Saini, Subhash↗
NASA NTRS1974

On the recognition of complex structures: Computer software using artificial intelligence applied to pattern recognition

An approach to simultaneous interpretation of objects in complex structures so as to maximize a combined utility function is presented. Results of the application of a computer software system to assign meaning to regions in a segmented image based on the principles described in this paper and on a special interactive sequential classification learning system, which is referenced, are demonstrated.

Yakimovsky, Y.↗
NASA NTRS1993

MACH 3: Past and future approaches to intelligent tutoring

In 1986, the U.S. Army Research Institute created an intelligent tutoring system as a proof-of-concept for artificial intelligence applications in Army training. The Maintenance Aid Computer HAWK Intelligent Institutional Instructor (MACH 3) taught student mechanics to maintain and troubleshoot the AN/MPQ-57 High Power Illuminator Radar (HPIR) of the HAWK Air Defense Missile System. In 1989, TRADOC Analysis Command compared the effectiveness of MACH 3 to traditional paper-based troubleshooting drills. For the study, all students received lecture and hands-on training as usual. However, during troubleshooting drills, students traced faults using either MACH 3 or the traditional paper-based method. Class records showed that the MACH 3 group completed significantly more troubleshooting tasks and progressed through tasks of greater difficulty than the paper-based group. Upon completion of training, students took written, practical, and oral essay tests. Mean test scores showed that students performed similarly regardless of the drill method used. However, significantly different standard deviations showed that the MACH 3 group performed more consistently than the paper-based group. Furthermore, significantly different time measures showed that the MACH 3 group reached faster troubleshooting solutions on the actual radar transmitter than the paper-based group. We will present the study results and discuss how updating the design of the MACH 3 can include desktop computing in a virtual environment.

Acchione-Noel, Sylvia↗
NASA NTRS1989

Artificial intelligence issues related to automated computing operations

Large data processing installations represent target systems for effective applications of artificial intelligence (AI) constructs. The system organization of a large data processing facility at the NASA Marshall Space Flight Center is presented. The methodology and the issues which are related to AI application to automated operations within a large-scale computing facility are described. Problems to be addressed and initial goals are outlined.

Hornfeck, William A.↗
NASA NTRS2000

Autonomy for Constellation

The newer types of space systems, which are planned for the future, are placing challenging demands for newer autonomy concepts and techniques. Motivating these challenges are resource constraints. Even though onboard computing power will surely increase in the coming years, the resource constraints associated with space-based processes will continue to be a major factor that needs to be considered when dealing with, for example, agent-based spacecraft autonomy. To realize "economical intelligence", i.e., constrained computational intelligence that can reside within a process under severe resource constraints (time, power, space, etc.), is a major goal for such space systems as the Nanosat constellations. To begin to address the new challenges, we are developing approaches to constellation autonomy with constraints in mind. Within the Agent Concepts Testbed (ACT) at the Goddard Space Flight Center we are currently developing a Nanosat-related prototype for the first of the two-step program.

Truszkowski, Walt↗
NASA NTRS1990

Is thinking computable?

Strong artificial intelligence claims that conscious thought can arise in computers containing the right algorithms even though none of the programs or components of those computers understand which is going on. As proof, it asserts that brains are finite webs of neurons, each with a definite function governed by the laws of physics; this web has a set of equations that can be solved (or simulated) by a sufficiently powerful computer. Strong AI claims the Turing test as a criterion of success. A recent debate in Scientific American concludes that the Turing test is not sufficient, but leaves intact the underlying premise that thought is a computable process. The recent book by Roger Penrose, however, offers a sharp challenge, arguing that the laws of quantum physics may govern mental processes and that these laws may not be computable. In every area of mathematics and physics, Penrose finds evidence of nonalgorithmic human activity and concludes that mental processes are inherently more powerful than computational processes.

Denning, Peter J.↗