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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
NASA NTRS1991

Systematic methods for knowledge acquisition and expert system development

Nine cooperating rule-based systems, collectively called AUTOCREW which were designed to automate functions and decisions associated with a combat aircraft's subsystems, are discussed. The organization of tasks within each system is described; performance metrics were developed to evaluate the workload of each rule base and to assess the cooperation between the rule bases. Simulation and comparative workload results for two mission scenarios are given. The scenarios are inbound surface-to-air-missile attack on the aircraft and pilot incapacitation. The methodology used to develop the AUTOCREW knowledge bases is summarized. Issues involved in designing the navigation sensor selection expert in AUTOCREW's NAVIGATOR knowledge base are discussed in detail. The performance of seven navigation systems aiding a medium-accuracy INS was investigated using Kalman filter covariance analyses. A navigation sensor management (NSM) expert system was formulated from covariance simulation data using the analysis of variance (ANOVA) method and the ID3 algorithm. ANOVA results show that statistically different position accuracies are obtained when different navaids are used, the number of navaids aiding the INS is varied, the aircraft's trajectory is varied, and the performance history is varied. The ID3 algorithm determines the NSM expert's classification rules in the form of decision trees. The performance of these decision trees was assessed on two arbitrary trajectories, and the results demonstrate that the NSM expert adapts to new situations and provides reasonable estimates of the expected hybrid performance.

Belkin, Brenda L.↗
NASA NTRS1988

Machine intelligence and autonomy for aerospace systems

The present volume discusses progress toward intelligent robot systems in aerospace applications, NASA Space Program automation and robotics efforts, the supervisory control of telerobotics in space, machine intelligence and crew/vehicle interfaces, expert-system terms and building tools, and knowledge-acquisition for autonomous systems. Also discussed are methods for validation of knowledge-based systems, a design methodology for knowledge-based management systems, knowledge-based simulation for aerospace systems, knowledge-based diagnosis, planning and scheduling methods in AI, the treatment of uncertainty in AI, vision-sensing techniques in aerospace applications, image-understanding techniques, tactile sensing for robots, distributed sensor integration, and the control of articulated and deformable space structures.

Heer, Ewald↗
NASA NTRS1990

Rule-based mechanisms of learning for intelligent adaptive flight control

How certain aspects of human learning can be used to characterize learning in intelligent adaptive control systems is investigated. Reflexive and declarative memory and learning are described. It is shown that model-based systems-theoretic adaptive control methods exhibit attributes of reflexive learning, whereas the problem-solving capabilities of knowledge-based systems of artificial intelligence are naturally suited for implementing declarative learning. Issues related to learning in knowledge-based control systems are addressed, with particular attention given to rule-based systems. A mechanism for real-time rule-based knowledge acquisition is suggested, and utilization of this mechanism within the context of failure diagnosis for fault-tolerant flight control is demonstrated.

Handelman, David A.↗
NASA NTRS1987

Intelligent process development of foam molding for the Thermal Protection System (TPS) of the space shuttle external tank

A knowledge based system to assist process engineers in evaluating the processability and moldability of poly-isocyanurate (PIR) formulations for the thermal protection system of the Space Shuttle external tank (ET) is discussed. The Reaction Injection Molding- Process Development Advisor (RIM-PDA) is a coupled system which takes advantage of both symbolic and numeric processing techniques. This system will aid the process engineer in identifying a startup set of mold schedules and in refining the mold schedules to remedy specific process problems diagnosed by the system.

Bharwani, S. S.↗
NASA NTRS2000

AI's Philosophical Underpinnings: A Thinking Person's Walk through the Twists and Turns of Artificial Intelligence's Meandering Path

Few human endeavors can be viewed both as extremely successful and unsuccessful at the same time. This is typically the case when goals have not been well defined or have been shifting in time. This has certainly been true of Artificial Intelligence (AI). The nature of intelligence has been the object of much thought and speculation throughout the history of philosophy. It is in the nature of philosophy that real headway is sometimes made only when appropriate tools become available. Similarly the computer, coupled with the ability to program (at least in principle) any function, appeared to be the tool that could tackle the notion of intelligence. To suit the tool, the problem of the nature of intelligence was soon sidestepped in favor of this notion: If a probing conversation with a computer could not be distinguished from a conversation with a human, then AI had been achieved. This notion became known as the Turing test, after the mathematician Alan Turing who proposed it in 1950. Conceptually rich and interesting, these early efforts gave rise to a large portion of the field's framework. Key to AI, rather than the 'number crunching' typical of computers until then, was viewed as the ability to manipulate symbols and make logical inferences. To facilitate these tasks, AI languages such as LISP and Prolog were invented and used widely in the field. One idea that emerged and enabled some success with real world problems was the notion that 'most intelligence' really resided in knowledge. A phrase attributed to Feigenbaum, one of the pioneers, was 'knowledge is the power.' With this premise, the problem is shifted from 'how do we solve problems' to 'how do we represent knowledge.' A good knowledge representation scheme could allow one to draw conclusions from given premises. Such schemes took forms such as rules,frames and scripts. It allowed the building of what became known as expert systems or knowledge based systems (KBS).

Colombano, Silvano↗
NASA NTRS1993

Reducing the cognitive workload: Trouble managing power systems

The complexity of space-based systems makes monitoring them and diagnosing their faults taxing for human beings. Mission control operators are well-trained experts but they can not afford to have their attention diverted by extraneous information. During normal operating conditions monitoring the status of the components of a complex system alone is a big task. When a problem arises, immediate attention and quick resolution is mandatory. To aid humans in these endeavors we have developed an automated advisory system. Our advisory expert system, Trouble, incorporates the knowledge of the power system designers for Space Station Freedom. Trouble is designed to be a ground-based advisor for the mission controllers in the Control Center Complex at Johnson Space Center (JSC). It has been developed at NASA Lewis Research Center (LeRC) and tested in conjunction with prototype flight hardware contained in the Power Management and Distribution testbed and the Engineering Support Center, ESC, at LeRC. Our work will culminate with the adoption of these techniques by the mission controllers at JSC. This paper elucidates how we have captured power system failure knowledge, how we have built and tested our expert system, and what we believe are its potential uses.

Manner, David B.↗
NASA NTRS1993

Reducing the cognitive workload - Trouble managing power systems

The complexity of space-based systems makes monitoring them and diagnosing their faults taxing for human beings. When a problem arises, immediate attention and quick resolution is mandatory. To aid humans in these endeavors we have developed an automated advisory system. Our advisory expert system, Trouble, incorporates the knowledge of the power system designers for Space Station Freedom. Trouble is designed to be a ground-based advisor for the mission controllers in the Control Center Complex at Johnson Space Center (JSC). It has been developed at NASA Lewis Research Center (LeRC) and tested in conjunction with prototype flight hardware contained in the Power Management and Distribution testbed and the Engineering Support Center, ESC, at LeRC. Our work will culminate with the adoption of these techniques by the mission controllers at JSC. This paper elucidates how we have captured power system failure knowledge, how we have built and tested our expert system, and what we believe its potential uses are.

Manner, David B.↗
NASA NTRS1988

Graphic simualtion test bed for robotics applications in a workstation environment

Graphical simulation is a cost-effective solution for developing and testing robots and their control systems. The availability of various high-performance workstations makes these systems feasible. Simulation offers preliminary testing of systems before their actual realizations, and it provides a framework for developing new control and planning algorithms. On the other hand, these simulation systems have to have the capability of incorporating various knowledge-based system components, e.g., task planners, representation formalisms, etc. They also should have an appropriate user interface, which makes possible the creation and control of simulation models. ROBOSIM was developed jointly by MSFC and Vanderbilt University, first in a VAX environment. Recently, the system has been ported to an HP-9000 workstation equipped with an SRX graphics accelerator. The user interface of the system now contains a menu- and icon-based facility, as well as the original ROBOSIM language. The system is also coupled to a symbolic computing system based on Common Lisp, where knowledge-based functionalities are implemented. The knowledge-based layer uses various representation and reasoning facilities for programming and testing the control systems of robots.

Springfield, J.↗
NASA NTRS1988

Using AGNESS (A Generalized Network-based Expert System Shell) for matching images

The image correspondence problem is considered the most difficult step in both stereo and motion analysis. Stereo vision is useful in determining the 3-D positions of points on visible surface in a scene. Motion analysis is useful in determining the spatial and temporal relationships of objects in an environment. Besides stereo and motion analysis, there is the image correspondence problem. Most of this work is based on point or local area properties of the observed gray level values in 2-D images. A global and general approach to this problem is described by using a knowledge-based system. The knowledge it uses consists of both physical properties and spatial relationships of the edges and regions extracted from the given images. The physical component depends on features of the edge or region) in isolation. The spatial component involves the set of edges and regions adjacent to a given edge (or region) of the first image and the set of edges and regions adjacent to each potentially matching edge (or region) of the second image; thus the spatial context of each edge or region is considered. A computational network is used to represent this knowledge, it allows the computation of the likelihood of matching two edges or regions with logical and heuristic operators. An expert system shell called AGNESS (A Generalized Network-based Expert System Shell) is used to build a prototype system.

Pong, Ting-Chuen↗
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 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 NTRS2003

Expert Seeker

Expert Seeker is a computer program of the knowledge-management-system (KMS) type that falls within the category of expertise-locator systems. The main goal of the KMS system implemented by Expert Seeker is to organize and distribute knowledge of who are the domain experts within and without a given institution, company, or other organization. The intent in developing this KMS was to enable the re-use of organizational knowledge and provide a methodology for querying existing information (including structured, semistructured, and unstructured information) in a way that could help identify organizational experts. More specifically, Expert Seeker was developed to make it possible, by use of an intranet, to do any or all of the following: Assist an employee in identifying who has the skills needed for specific projects and to determine whether the experts so identified are available. Assist managers in identifying employees who may need training opportunities. Assist managers in determining what expertise is lost when employees retire or otherwise leave. Facilitate the development of new ways of identifying opportunities for innovation and minimization of duplicated efforts. Assist employees in achieving competitive advantages through the application of knowledge-management concepts and related systems. Assist external organizations in requesting speakers for specific engagements or determining from whom they might be able to request help via electronic mail. Help foster an environment of collaboration for rapid development in today's environment, in which it is increasingly necessary to assemble teams of experts from government, universities, research laboratories, and industries, to quickly solve problems anytime, anywhere. Make experts more visible. Provide a central repository of information about employees, including information that, heretofore, has typically not been captured by the human-resources systems (e.g., information about past projects, patents, or hobbies). Unify myriad collections of data into Web-enabled repository that could easily be searched for relevant data.

Fernandez, Becerra↗
NASA NTRS1988

Avionic expert systems

At the heart of any intelligent flight control system, there is a knowledge based expert system. The efficiency of these knowledge bases is one of the major factors in the success of aviation and space control systems. In the future, the speed and the capabilities of the expert system and their underlying data base(s) will be the limiting factors in the ability to build more accurate real time space controllers. A methodology is proposed for design and construction of such expert systems. It is noted that existing expert systems are inefficient (slow) in dealing with nontrivial real world situations that involve a vast collection of data. However, current data bases, which are fast in handling large amounts of data, cannot carry out intelligent tasks normally expected from an expert system. The system presented provides the power of deduction (reasoning) along with the efficient mechanisms for management of large data bases. In the system, both straight forward evaluation procedures and sophisticated inference mechanisms coexist. The design methodology is based on mathematics and logic, which ensures the correctness of the final product.

Golshani, Forouzan↗
DOE OSTI2024

Deep Learning-Based Dynamic Modeling of Three-Phase Voltage Source Inverters

Inverter-based resource (IBR) models are necessary to analyze modern power system stability and create effective control strategies. Modeling IBRs in converter-rich power systems is crucial, yet challenging due to the lack of commercial information on converter topologies and control parameters. This paper proposes novel convolutional neural network (CNN)–based data-driven techniques for modeling IBRs, addressing adaptability and proprietary concerns without requiring internal system physics knowledge. The proposed method is tested using real grid-tied commercial IBR transient data and demonstrates effectiveness and accuracy. Furthermore, the developed modeling approach is integrated and implemented in the open-source power distribution simulation and analysis tool, GridLAB-D, to illustrate the potentiality of dynamic analysis of large-scale power systems with high IBRs.

deep learning, artificial intelligence↗
NASA NTRS1991

Quantitative knowledge acquisition for expert systems

A common problem in the design of expert systems is the definition of rules from data obtained in system operation or simulation. While it is relatively easy to collect data and to log the comments of human operators engaged in experiments, generalizing such information to a set of rules has not previously been a direct task. A statistical method is presented for generating rule bases from numerical data, motivated by an example based on aircraft navigation with multiple sensors. The specific objective is to design an expert system that selects a satisfactory suite of measurements from a dissimilar, redundant set, given an arbitrary navigation geometry and possible sensor failures. The systematic development is described of a Navigation Sensor Management (NSM) Expert System from Kalman Filter convariance data. The method invokes two statistical techniques: Analysis of Variance (ANOVA) and the ID3 Algorithm. The ANOVA technique indicates whether variations of problem parameters give statistically different covariance results, and the ID3 algorithms identifies the relationships between the problem parameters using probabilistic knowledge extracted from a simulation example set. Both are detailed.

Belkin, Brenda L.↗
NASA NTRS1991

Knowledge acquisition for expert systems using statistical methods

A common problem in the design of expert systems is the definition of rules from data obtained in system operation or simulation. A statistical method for generating rule bases from numerical data, motivated by an example based on aircraft navigation with multiple sensors is presented. The specific objective is to design an expert system that selects a satisfactory suite of measurements from a dissimilar, redundant set, given an arbitrary navigation geometry and possible sensor failures. The systematic development of a Navigation Sensor Management (NSM) Expert System from Kalman Filter covariance data is described. The development method invokes two statistical techniques: Analysis-of-Variance (ANOVA) and the ID3 algorithm. The ANOVA technique indicates whether variations of problem parameters give statistically different covariance results, and the ID3 algorithm identifies the relationships between the problem parameters using probabilistic knowledge extracted from a simulation example set.

Belkin, Brenda L.↗
NASA NTRS1993

Automated documentation generator for advanced protein crystal growth

The System Management and Production Laboratory at the Research Institute, the University of Alabama in Huntsville (UAH), was tasked by the Microgravity Experiment Projects (MEP) Office of the Payload Projects Office (PPO) at Marshall Space Flight Center (MSFC) to conduct research in the current methods of written documentation control and retrieval. The goals of this research were to determine the logical interrelationships within selected NASA documentation, and to expand on a previously developed prototype system to deliver a distributable, electronic knowledge-based system. This computer application would then be used to provide a paperless interface between the appropriate parties for the required NASA document.

Maddux, Gary A.↗
NASA NTRS1993

An expert system shell for inferring vegetation characteristics: Prototype help system (Task 1)

The NASA Vegetation Workbench (VEG) is a knowledge based system that infers vegetation characteristics from reflectance data. A prototype of the VEG subgoal HELP.SYSTEM has been completed and the Help System has been added to the VEG system. It is loaded when the user first clicks on the HELP.SYSTEM option in the Tool Box Menu. The Help System provides a user tool to support needed user information. It also provides interactive tools the scientist may use to develop new help messages and to modify existing help messages that are attached to VEG screens. The system automatically manages system and file operations needed to preserve new or modified help messages. The Help System was tested both as a help system development and a help system user tool.

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