Learning Missing Mechanisms in a Dynamical System from a Subset of State Variable Observations.
Abstract not provided.
SEARCH · Search NASA
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Abstract not provided.
Abstract not provided.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
The Communications Link Expert Assistance Resource (CLEAR) is a fault-isolation expert system to be utilized in the operational environment of the Cosmic Background Explorer (COBE) Mission Operations Room (MOR). CLEAR will assist the COBE Flight Operations Team (FOT) during periods of real-time data acquisition by isolating faults in the spacecraft communication link with the Tracking and Data Relay Satellite (TDRS), providing advice on how to correct them, and logging the events for post-pass evaluation. After a brief introduction to the problem domain, the system requirements, tool selection, development approach, system operation and lessons learned during the transformation of the system from the prototype to the delivered, operation system are described.
It is pointed out that by merging the advantages of fuzzy expert systems and neural networks one can arrive at a more powerful yet more flexible system for inferencing and learning. The advantages of fuzzy expert systems are their ability to provide nonlinear mapping through the membership functions and fuzzy rules, and the ability to deal with fuzzy information and incomplete and/or imprecise data. The merger of these two concepts is explained using the truck backer-upper control problem. Novel network architectures obtained by merging these two concepts and simulation results for the truck backer-upper problem using the architecture are shown.
Software development organizations, both commercial and governmental, are undergoing rapid change spurred by developments in the computing industry. To stay competitive, these organizations must adopt new technologies, skills, and practices quickly. Yet even for an organization with a well-developed set of software engineering models and processes, transitioning to a new technology can be expensive and risky. Current industry trends are leading away from traditional mainframe environments and toward the workstation-based, open systems world. This paper presents the experiences of software engineers on three recent projects that pioneered open systems development for NASA's Flight Dynamics Division of the Goddard Space Flight Center (GSFC).
Primary Goal of this project is to (1) Design/build a flexible system to monitor air contamination (2) Learn requirements for operating system in low pressure and low temperature environments (3) Design/build system for integration into aircraft and automobiles Secondary Goals/Offshoots are (1) Fly aboard different aircraft (2)Hand-carry unit (3) Drive unit in automobiles.
No abstract available
Abstract—Natural human capacities are becoming increasingly mismatched to the enormous data volumes, processing capabilities, and decision speeds demanded in today’s aviation environment. Increasingly Autonomous Systems (IAS) are uniquely suited to solve this problem. NASA is conducting research and development of IAS - hardware and software systems, utilizing machine learning algorithms, seamlessly integrated with humans whereby task performance of the combined system is significantly greater than the individual components. IAS offer the potential for significantly improved levels of performance and safety that are superior to either human or automation alone. A human-in-the-loop test was conducted in NASA Langley’s Integration Flight Deck B-737-800 simulator to evaluate advanced synoptic pages with simplified interactive electronic checklists as an IAS for routine air carrier flight operations and in response to aircraft system failures. Twelve U.S. airline crews flew various normal and non-normal procedures and their actions and performance were recorded in response to failures. These data are fundamental to and critical for the design and development of future increasingly autonomous systems that can better support the human in the cockpit. Synoptic pages and electronic checklists significantly improved pilot responses to non-normal scenarios, but implementation of these aids and other intelligent assistants have barriers to implementation (e.g., certification cost) that must overcome.
UNKNOWN
Explore the source record for details and available documents.
Abstract not provided.
Abstract not provided.
Abstract not provided.
We are developing, innovating and de-risking a biomethanation process capable of megawatt-scale deployment that upgrades biogas waste streams to produce pipeline quality renewable natural gas (RNG). Biomethanation is a two-step process using a methanogenic microorganism to convert renewable hydrogen (H2) and waste carbon dioxide (CO2) to renewable methane (CH4) - the primary component in natural gas. Using biogenic CO2 from biogas sources like dairies, wastewater treatment plants, and landfills allows production of this drop-in direct replacement fuel to participate in the growing number of carbon markets; like California's Low Carbon Fuel Standard and the Federal Renewable Fuel Standard. The end-of-project goal is to demonstrate pipeline quality RNG production (> 95% CH4, < 4% H2, <1% CO2, < 0.2% O2 and < 4 parts per million hydrogen sulfide) using real biogas feedstocks. We will accomplish this goal by designing and building a pressurized (18 bar) mobile lab-scale (20L) bioreactor research platform, including integrated electrolyzer system, based on lessons learned from operating the 700L pilot system from Southern California Gas Company. In collaboration with Electrochaea, natural gas utilities and Argonne National lab, will provide the data to establish a preliminary range of carbon intensity to help accelerate the deployment of utility-scale H2 production and qualify the biomethanation pathway process for RNG production.
Abstract not provided.