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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

A Brief Overview of NASA Glenn Research Center Sensor and Electronics Activities

Aerospace applications require a range of sensing technologies. There is a range of sensor and sensor system technologies being developed using microfabrication and micromachining technology to form smart sensor systems and intelligent microsystems. Drive system intelligence to the local (sensor) level -- distributed smart sensor systems. Sensor and sensor system development examples: (1) Thin-film physical sensors (2) High temperature electronics and wireless (3) "lick and stick" technology. NASA GRC is a world leader in aerospace sensor technology with a broad range of development and application experience. Core microsystems technology applicable to a range of application environmentS.

Hunter, Gary W.↗
NASA NTRS1986

Implementation of artificial intelligence rules in a data base management system

The intelligent front end prototype was transformed into a RIM-integrated system. A RIM-based expert system was written which demonstrated the developed capability. The use of rules to produce extensibility of the intelligent front end, including the concept of demons and rule manipulation rules were investigated. Innovative approaches such as syntax programming were to be considered.

Feyock, S.↗
DOE OSTI2017

Statistical Hypothesis Testing using CNN Features for Synthesis of Adversarial Counterexamples to Human and Object Detection Vision Systems

Validating the correctness of human detection vision systems is crucial for safety applications such as pedestrian collision avoidance in autonomous vehicles. The enormous space of possible inputs to such an intelligent system makes it difficult to design test cases for such systems. In this report, we present our tool MAYA that uses an error model derived from a convolutional neural network (CNN) to explore the space of images similar to a given input image, and then tests the correctness of a given human or object detection system on such perturbed images. We demonstrate the capability of our tool on the pre-trained Histogram-of-Oriented-Gradients (HOG) human detection algorithm implemented in the popular OpenCV toolset and the Caffe object detection system pre-trained on the ImageNet benchmark. Our tool may serve as a testing resource for the designers of intelligent human and object detection systems.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2004

Controls and Health Management Technologies for Intelligent Aerospace Propulsion Systems

With the increased emphasis on aircraft safety, enhanced performance and affordability, and the need to reduce the environmental impact of aircraft, there are many new challenges being faced by the designers of aircraft propulsion systems. The Controls and Dynamics Technology Branch at NASA (National Aeronautics and Space Administration) Glenn Research Center (GRC) in Cleveland, Ohio, is leading and participating in various projects in partnership with other organizations within GRC and across NASA, the U.S. aerospace industry, and academia to develop advanced controls and health management technologies that will help meet these challenges through the concept of an Intelligent Engine. The key enabling technologies for an Intelligent Engine are the increased efficiencies of components through active control, advanced diagnostics and prognostics integrated with intelligent engine control to enhance component life, and distributed control with smart sensors and actuators in an adaptive fault tolerant architecture. This paper describes the current activities of the Controls and Dynamics Technology Branch in the areas of active component control and propulsion system intelligent control, and presents some recent analytical and experimental results in these areas.

Garg, Sanjay↗
NASA NTRS2003

Robot Tracking of Human Subjects in Field Environments

Future planetary exploration will involve both humans and robots. Understanding and improving their interaction is a main focus of research in the Intelligent Systems Branch at NASA's Johnson Space Center. By teaming intelligent robots with astronauts on surface extra-vehicular activities (EVAs), safety and productivity can be improved. The EVA Robotic Assistant (ERA) project was established to study the issues of human-robot teams, to develop a testbed robot to assist space-suited humans in exploration tasks, and to experimentally determine the effectiveness of an EVA assistant robot. A companion paper discusses the ERA project in general, its history starting with ASRO (Astronaut-Rover project), and the results of recent field tests in Arizona. This paper focuses on one aspect of the research, robot tracking, in greater detail: the software architecture and algorithms. The ERA robot is capable of moving towards and/or continuously following mobile or stationary targets or sequences of targets. The contributions made by this research include how the low-level pose data is assembled, normalized and communicated, how the tracking algorithm was generalized and implemented, and qualitative performance reports from recent field tests.

Graham, Jeffrey↗
DOE OSTI2012

Verification of Adaptive Systems

Adaptive systems are critical for future space and other unmanned and intelligent systems. Verification of these systems is also critical for their use in systems with potential harm to human life or with large financial investments. Due to their nondeterministic nature and extremely large state space, current methods for verification of software systems are not adequate to provide a high level of assurance for them. The combination of stabilization science, high performance computing simulations, compositional verification and traditional verification techniques, plus operational monitors, provides a complete approach to verification and deployment of adaptive systems that has not been used before. This paper gives an overview of this approach.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2012

The AdaptiV Approach to Verification of Adaptive Systems

Adaptive systems are critical for future space and other unmanned and intelligent systems. Verification of these systems is also critical for their use in systems with potential harm to human life or with large financial investments. Due to their nondeterministic nature and extremely large state space, current methods for verification of software systems are not adequate to provide a high level of assurance. The combination of stabilization science, high performance computing simulations, compositional verification and traditional verification techniques, plus operational monitors, provides a complete approach to verification and deployment of adaptive systems that has not been used before. This paper gives an overview of this approach.

99 GENERAL AND MISCELLANEOUS↗
NASA NTRS1987

Knowledge representation by connection matrices: A method for the on-board implementation of large expert systems

Extremely large knowledge sources and efficient knowledge access characterizing future real-life artificial intelligence applications represent crucial requirements for on-board artificial intelligence systems due to obvious computer time and storage constraints on spacecraft. A type of knowledge representation and corresponding reasoning mechanism is proposed which is particularly suited for the efficient processing of such large knowledge bases in expert systems.

Kellner, A.↗
NASA NTRS2007

F-15 IFCS: Intelligent Flight Control System

This viewgraph presentation describes the F-15 Intelligent Flight Control System (IFCS). The goals of this project include: 1) Demonstrate revolutionary control approaches that can efficiently optimize aircraft performance in both normal and failure conditions; and 2) Demonstrate advance neural network-based flight control technology for new aerospace systems designs.

Bosworth, John↗
NASA NTRS1991

Multispectral Image Processing for Plants

The development of a machine vision system to monitor plant growth and health is one of three essential steps towards establishing an intelligent system capable of accurately assessing the state of a controlled ecological life support system for long-term space travel. Besides a network of sensors, simulators are needed to predict plant features, and artificial intelligence algorithms are needed to determine the state of a plant based life support system. Multispectral machine vision and image processing can be used to sense plant features, including health and nutritional status.

Miles, Gaines E.↗
NASA NTRS1993

Mixed data/goal driven intelligent real-time assessment and control

We describe work in progress to develop model-based systems for real-time assessment and control of complex systems. Intelligent real-time processing must balance accuracy and response-time requirements to maximize expected performance. We use brief (probability) and goal (utility) information to guide construction of an approximate system model on which we then apply approximate inference procedures to yield timely, effective assessments and control decisions.

Dambrosio, Bruce↗
NASA NTRS1990

Knowledge representation into Ada parallel processing

The Knowledge Representation into Ada Parallel Processing project is a joint NASA and Air Force funded project to demonstrate the execution of intelligent systems in Ada on the Charles Stark Draper Laboratory fault-tolerant parallel processor (FTPP). Two applications were demonstrated - a portion of the adaptive tactical navigator and a real time controller. Both systems are implemented as Activation Framework Objects on the Activation Framework intelligent scheduling mechanism developed by Worcester Polytechnic Institute. The implementations, results of performance analyses showing speedup due to parallelism and initial efficiency improvements are detailed and further areas for performance improvements are suggested.

Masotto, Tom↗
DOE OSTI2021

Evolution of the IEEE P7009 Standard: Towards Fail-Safe Design of Autonomous Systems

Autonomous systems are complex, diverse, and likely be used in environments that change unpredictably. We must develop mechanisms to cope with failures, potentially caused by both the natural stresses of the operating environment and the system's inadequate environmental models. Failures also arise from system component wear. In 2016, the IEEE launched the Global Initiative on Ethics of Autonomous and Intelligent Systems in response to the proliferation of increasingly-autonomous systems. A cornerstone of this initiative is the IEEE P7000 series of projects, which set the standards for the future of ethical intelligent and autonomous technologies. This paper describes the ongoing development of the IEEE P7009 standard on “Fail-Safe Design of Autonomous and Semi-Autonomous Systems”. We provide a collective perspective on the ongoing development of IEEE P7009, and outline the objectives, current approaches, issues under discussion, and two exemplar use cases from different sectors: one high-regulation, the other low-regulation.

Farrell, Marie↗
NASA NTRS2007

Software Sharing Enables Smarter Content Management

In 2004, NASA established a technology partnership with Xerox Corporation to develop high-tech knowledge management systems while providing new tools and applications that support the Vision for Space Exploration. In return, NASA provides research and development assistance to Xerox to progress its product line. The first result of the technology partnership was a new system called the NX Knowledge Network (based on Xerox DocuShare CPX). Created specifically for NASA's purposes, this system combines Netmark-practical database content management software created by the Intelligent Systems Division of NASA's Ames Research Center-with complementary software from Xerox's global research centers and DocuShare. NX Knowledge Network was tested at the NASA Astrobiology Institute, and is widely used for document management at Ames, Langley Research Center, within the Mission Operations Directorate at Johnson Space Center, and at the Jet Propulsion Laboratory, for mission-related tasks.

Source record↗
NASA NTRS1964

Artificial intelligence

Artificial intelligence - system for instructing computer and programming language for manipulating symbolic expressions

ARTIFICIAL INTELLIGENCE↗
NASA NTRS2006

System Analysis Applied to Autonomy: Application to High-Altitude Long-Endurance Remotely Operated Aircraft

Maturation of intelligent systems technologies and their incorporation into aerial platforms are dictating the development of new analysis tools and incorporation of such tools into existing system analysis methodologies in order to fully capture the trade-offs of autonomy on vehicle and mission success. A first-order "system analysis of autonomy" methodology is outlined in this paper. Further, this analysis methodology is subsequently applied to notional high-altitude long-endurance (HALE) aerial vehicle missions.

Young, Larry A.↗
NASA NTRS2012

A Model-Based Approach to Engineering Behavior of Complex Aerospace Systems

One of the most challenging yet poorly defined aspects of engineering a complex aerospace system is behavior engineering, including definition, specification, design, implementation, and verification and validation of the system's behaviors. This is especially true for behaviors of highly autonomous and intelligent systems. Behavior engineering is more of an art than a science. As a process it is generally ad-hoc, poorly specified, and inconsistently applied from one project to the next. It uses largely informal representations, and results in system behavior being documented in a wide variety of disparate documents. To address this problem, JPL has undertaken a pilot project to apply its institutional capabilities in Model-Based Systems Engineering to the challenge of specifying complex spacecraft system behavior. This paper describes the results of the work in progress on this project. In particular, we discuss our approach to modeling spacecraft behavior including 1) requirements and design flowdown from system-level to subsystem-level, 2) patterns for behavior decomposition, 3) allocation of behaviors to physical elements in the system, and 4) patterns for capturing V&V activities associated with behavioral requirements. We provide examples of interesting behavior specification patterns, and discuss findings from the pilot project.

SysML↗
NASA NTRS2007

F-15 837 IFCS Intelligent Flight Control System Project

This viewgraph presentation reviews the use of Intelligent Flight Control System (IFCS) for the F-15. The goals of the project are: (1) Demonstrate Revolutionary Control Approaches that can Efficiently Optimize Aircraft Performance in both Normal and Failure Conditions (2) Advance Neural Network-Based Flight Control Technology for New Aerospace Systems Designs. The motivation for the development are to reduce the chance and skill required for survival.

Bosworth, John T.↗