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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 235 records · Page 13

All sources NASA NTRS DOE OSTI

NASA NTRS1979

Software design and documentation language, revision 1

The Software Design and Documentation Language (SDDL) developed to provide an effective communications medium to support the design and documentation of complex software applications is described. Features of the system include: (1) a processor which can convert design specifications into an intelligible, informative machine-reproducible document; (2) a design and documentation language with forms and syntax that are simple, unrestrictive, and communicative; and (3) methodology for effective use of the language and processor. The SDDL processor is written in the SIMSCRIPT II programming language and is implemented on the UNIVAC 1108, the IBM 360/370, and Control Data machines.

Kleine, H.↗

NASA NTRS1977

A vehicle for developing standards for simulation programming

The objective of SDDL (Software Design and Documentation Language) is to provide an effective communications medium to support the design and documentation of complex software applications. This objective is met by providing (1) a processor which can express design specifications in an intelligible, informative, machine-reproducible document, (2) a design and documentation language with forms and syntax that are simple, unrestrictive, and communicative, and (3) methodology for effective use of the language and processor. The application of SDDL to the specific problems of simulation models is discussed, with emphasis on the potential of SDDL for developing and specifying design and documentation standards for simulation and modeling.

Kleine, H.↗

NASA NTRS1989

Three-Dimensional Robotic Vision System

Stereoscopy and motion provide clues to outlines of objects. Digital image-processing system acts as "intelligent" automatic machine-vision system by processing views from stereoscopic television cameras into three-dimensional coordinates of moving object in view. Epipolar-line technique used to find corresponding points in stereoscopic views. Robotic vision system analyzes views from two television cameras to detect rigid three-dimensional objects and reconstruct numerically in terms of coordinates of corner points. Stereoscopy and effects of motion on two images complement each other in providing image-analyzing subsystem with clues to natures and locations of principal features.

Nguyen, Thinh V.↗

NASA NTRS1988

Systems autonomy

Information on systems autonomy is given in viewgraph form. Information is given on space systems integration, intelligent autonomous systems, automated systems for in-flight mission operations, the Systems Autonomy Demonstration Project on the Space Station Thermal Control System, the architecture of an autonomous intelligent system, artificial intelligence research issues, machine learning, and real-time image processing.

Lum, Henry, Jr.↗

NASA NTRS1987

An overview of the artificial intelligence and expert systems component of RICIS

Artificial Intelligence and Expert Systems are the important component of RICIS (Research Institute and Information Systems) research program. For space applications, a number of problem areas that should be able to make good use of the above tools include: resource allocation and management, control and monitoring, environmental control and life support, power distribution, communications scheduling, orbit and attitude maintenance, redundancy management, intelligent man-machine interfaces and fault detection, isolation and recovery.

Feagin, Terry↗

NASA NTRS2002

Autonomous planning and scheduling on the TechSat 21 mission

The Autonomous Sciencecraft Experiment (ASE) will fly onboard the Air Force TechSat 21 constellation of three spacecraft scheduled for launch in 2006. ASE uses onboard continuous planning, robust task and goal-based execution, model-based mode identification and reconfiguration, and onboard machine learning and pattern recognition to radically increase science return by enabling intelligent downlink selection and autonomous retargeting.

AI architectures applications intelligent agents m↗

NASA NTRS2009

Adaptive Morphological Feature-Based Object Classifier for a Color Imaging System

Utilizing a Compact Color Microscope Imaging System (CCMIS), a unique algorithm has been developed that combines human intelligence along with machine vision techniques to produce an autonomous microscope tool for biomedical, industrial, and space applications. This technique is based on an adaptive, morphological, feature-based mapping function comprising 24 mutually inclusive feature metrics that are used to determine the metrics for complex cell/objects derived from color image analysis. Some of the features include: Area (total numbers of non-background pixels inside and including the perimeter), Bounding Box (smallest rectangle that bounds and object), centerX (x-coordinate of intensity-weighted, center-of-mass of an entire object or multi-object blob), centerY (y-coordinate of intensity-weighted, center-of-mass, of an entire object or multi-object blob), Circumference (a measure of circumference that takes into account whether neighboring pixels are diagonal, which is a longer distance than horizontally or vertically joined pixels), . Elongation (measure of particle elongation given as a number between 0 and 1. If equal to 1, the particle bounding box is square. As the elongation decreases from 1, the particle becomes more elongated), . Ext_vector (extremal vector), . Major Axis (the length of a major axis of a smallest ellipse encompassing an object), . Minor Axis (the length of a minor axis of a smallest ellipse encompassing an object), . Partial (indicates if the particle extends beyond the field of view), . Perimeter Points (points that make up a particle perimeter), . Roundness [(4(pi) x area)/perimeter(squared)) the result is a measure of object roundness, or compactness, given as a value between 0 and 1. The greater the ratio, the rounder the object.], . Thin in center (determines if an object becomes thin in the center, (figure-eight-shaped), . Theta (orientation of the major axis), . Smoothness and color metrics for each component (red, green, blue) the minimum, maximum, average, and standard deviation within the particle are tracked. These metrics can be used for autonomous analysis of color images from a microscope, video camera, or digital, still image. It can also automatically identify tumor morphology of stained images and has been used to detect stained cell phenomena (see figure).

McDowell, Mark↗

NASA NTRS2019

Cognitive Communications and Networking Technology Infusion Study Report

As the envisioned next-generation SCaN Network transitions into an end-to-end “system of systems” with new enabling capabilities, it is anticipated that the introduction of machine learning, artificial intelligence, and other cognitive strategies into the network infrastructure will result in increased mission science return, improved resource efficiencies, and increased autonomy and reliability. This enhanced set of cognitive capabilities will be implemented via a “space cloud” concept to achieve a service-oriented architecture with distributed cognition, de-centralized routing, and shared, on-orbit data processing. The enabling cognitive communications and networking capabilities that may facilitate the desired network enhancements are identified in this document, and the associated enablers of these capabilities, such as technologies and standards, are described in detail.

Knoblock, Eric J.↗

NASA NTRS2019

Lunar Development Lab (LDL) Concept Leading to the First Human Lunar Outpost

The Lunar Development Lab (LDL) is a new concept to bring together academia, industry, non-profit organizations and NASA in an accelerator environment to generate new design solutions, technologies and architectures that will lead to the first human lunar outpost. By leveraging key partnerships in lunar science, mining, construction, chemical engineering and other key fields as well as making available rapid design, economic analysis, artificial intelligence (AI) and machine learning (ML) tools, significant progress can be made in a short amount of time. Therefore, the goal of LDL is to accelerate development and focus on economic solutions that can lead to sustainable and economical human lunar outpost.

Zuniga, Allison↗

NASA NTRS2019

Overcoming the Challenges of Data Integration and Automation

Artificial Intelligence (AI) and machine learning (ML) are gaining increased attention as a way to leverage the world's data to solve problems. Although AI and ML offer much potential, there are often misconceptions about the application of such techniques. This panel discussion includes speakers from airlines and the research community who will present machine learning approaches they have developed on a variety of aviation data including digital flight data, safety reporting data, and traffic flow data. They will explain the purpose of the application, the data used, and the lessons learned in the development and deployment of their solutions. The panel discussion will focus on common pitfalls in developing and AI solution, the dangers of the current hype around AI, tips for gaining value from a machine learning solution, how to determine whether a machine learning approach is appropriate for a problem, and more.

Matthews, Bryan L.↗

NASA NTRS2019

Artificial Intelligence in Aviation Safety Applications - Exploring Myths and Truths of AI and ML

Artificial Intelligence (AI) and machine learning (ML) are gaining increased attention as ways to leverage the world's data to solve problems. Although AI and ML offer much potential, there are often misconceptions about the application of such techniques.Panel speakers will present machine learning approaches they have developed on a variety of aviation data, including digital flight data, safety reporting data, and voice communications data. They will discuss the purpose of the application, the data used, and the lessons learned in the development and deployment of their solutions. The panel will also discuss common pitfalls in developing an AI solution, the dangers of the current hype around AI, tips for gaining value from a ML solution, how to determine whether a ML approach is appropriate for a problem, and more.

Reeves, Scott (Capt.)↗

NASA NTRS2020

Battery Health Quantification for TDRS Spacecraft by Using Signature Discriminability Measurement

The NASA/GSFC Space Network Project Office (SN) currently operates a constellation of ten geosynchronous TDRS spacecraft launched over the past 30 years. The SN project collects up to 16.5 Gigabytes of telemetry every month. Generally, the spacecraft health and functionality are obtained by the use of real-time telemetry data for the multiple spacecraft subsystems, which are transmitted to the main ground station at the White Sands Complex in Las Cruces, NM. Recently, the SN has instituted a program of Big Data to analyze the large amounts of data using a variety of tools including Machine Learning, Artificial Intelligence, development of training sets, and a variety of mathematical modeling tools. The goal is to improve spacecraft management and obtain a more accurate prediction of the spacecraft end of life. The combination of these efforts with those of the Aerospace Corporation, which has a contract with the SN to produce yearly reliability estimates for the TDRS fleet, will be performed. This paper presents a new concept called telemetry quality quantification (TQQ) and discusses the progress that has been made in battery performance estimation for the second-generation TDRS spacecraft using a signature discriminability measures (SDM) algorithm combined with the Aerospace Corp. battery life estimation models. This activity is important because many of the TDRS fleet of spacecraft have exceeded their on-orbit design lifetime and, therefore, NASA must carefully manage the spacecraft to continue operations while avoiding an end-of-mission scenario that leaves a non-functioning spacecraft in geosynchronous orbit.

Ma, Kenneth Y.↗

NASA NTRS2022

Run Time Assurance for Electric Vertical Takeoff and Landing Aircraft

NASA is conducting research to demonstrate and evaluate the application of Run Time Assurance (RTA) as a means to assure safety in Electric Vertical Takeoff and Landing (eVTOL) aircraft with highly automated or autonomous flight capability supervised by a single onboard pilot. The work described in this report demonstrates an application of RTA and examines the implications for design and analysis of aircraft functions and systems; aircraft safety hazards; safety assurance; development assurance; and pilot tasks and performance. This research effort also seeks to assess the efficacy of the combined application of traditional Functional Hazard Analysis (FHA) and the more modern System Theoretic Process Analysis (STPA) techniques to perform hazard analyses on aircraft with complex automated and autonomous systems and an onboard pilot. During the research effort we developed architectural designs of two alternate eVTOL aircraft, generally following the process characterized in the SAE standards ARP4754 and ARP4761. The design has focused on the control architectures of these aircraft, which are identical except that one incorporates RTA techniques to reduce the criticality of some key software components. Artifacts of this process include a taxonomy of aircraft-level functions, aircraft-level architecture diagrams, aircraft-level functional hazard assessments (AFHA), function allocations onto aircraft systems and subsystems, functional block diagrams for a select set of control-related functions, and system-level functional hazard assessments (SFHA) for those functions. This project has highlighted the notion that DAL D is something of a sweet spot for low-confidence controllers in an RTA-based design. Among the many activities described in DO-178C, the activities related to requirement verifiability, algorithmic accuracy, and test coverage can be the most challenging for the kinds of advanced control techniques that may be desirable in novel UAM designs, such as adaptive control, machine-learning, artificial intelligence, numerical search, and Monte Carlo based algorithms. Moreover, the standard requires that development teams demonstrate that errors leading to unacceptable failure conditions have been removed from the software. The RTA architecture, which cordons off the low-confidence function, makes it much easier to show this for these kinds of algorithms. With regard to the use of STPA and FHA as complementary hazard analysis techniques, our research effort led us to the conclusion that STPA should be used to derive requirements for hardware and software systems and/or components. Also, STPA is a natural complement to other processes in ARP4754A involving design studies and iteration.

Run-time assurance↗

NASA NTRSDate not supplied

Architecting Safer Autonomous Aviation Systems

The aviation literature gives relatively little guidance to practitioners about the specifics of architecting systems for safety, particularly the impact of architecture on allocating safety requirements, or the relative ease of system assurance resulting from system or subsystem level architectural choices. As an exemplar, this paper considers common architectural patterns used within traditional aviation systems and explores their safety and safety assurance implications when applied in the context of integrating artificial intelligence (AI) and machine learning (ML) based functionality. Considering safety as an architectural property, we discuss both the allocation of safety requirements and the architectural trade-offs involved early in the design lifecycle. This approach could be extended to other assured properties, similar to safety, such as security. We conclude with a discussion of the safety considerations that emerge in the context of candidate architectural patterns that have been proposed in the recent literature for enabling autonomy capabilities by integrating AI and ML. A recommendation is made for the generation of a property-driven architectural pattern catalogue.

Architecture patterns↗

NASA NTRSDate not supplied

Architecting Safer Autonomous Aviation Systems

Architecture patterns↗

NASA NTRSDate not supplied

Artificial Intelligence for Future Earth Science Information Systems and Earth System Digital Twins

NASA's Advanced Information Systems Technology (AIST) Program is one of several Technology programs managed by the Earth Science Technology Office (ESTO) in the Earth Science Division (ESD). The AIST Program focuses on advanced information systems and novel computer science technologies that will be needed by NASA Earth Science in the next 5 to 10 years. The three main thrusts of the AIST Program deal with Novel Observing Strategies (NOS), Analytic Collaborative Frameworks (ACF) and Earth System Digital Twins (ESDT). This presentation summarizes the AIST Program with a special focus on the Artificial Intelligence (AI) and Machine Learning (ML) technologies included in its funded projects.

Earth Science Remote Sensing; Information Systems↗

NASA NTRSDate not supplied

Advancing Open Source Science Initiatives Through Public-Private Partnerships

Collaboration is fundamental to advancing open science within the science community. With the recent developments in technology and research, the establishment of formal partnerships between the private sector and government agencies are needed to bridge the knowledge gaps and expedite the time to actionable science. NASA’s Interagency Implementation and Advanced Concepts Team (IMPACT) seeks to address this challenge by establishing non-reimbursable Space Act Agreements with industry leaders in cloud computing, artificial intelligence (AI) and machine learning. The purpose of these agreements is to advance open source science initiatives in the areas of data discovery, access and use of high value NASA science data sets on the cloud. As well as, jointly work on common research problems to accelerate the development and adoption of new AI technologies. Current success stories include co-locating NASA datasets from multiple science disciplines on one platform using Amazon Web Services Open Data Registry, developing AI Foundation Models for Science with IBM and co-hosting training workshops and tutorials for the science community aimed at providing hands-on experience with using NASA data and AI models on the cloud. In summary, we will present an overview of our partnerships supporting open source science initiatives, describe current activities and lessons learned that may be useful to others considering similar partnerships with the private sector.

Elizabeth Fancher↗

NASA NTRSDate not supplied

Novel Observing Strategies (NOS) and Earth System Digital Twins (ESDT) for Disaster Resilience

"Earth systems have now been observed continuously for more than 50 years, not only from space but also from airplanes, balloons and in-situ sensors. With the addition of commercial remote sensing providers, many more Internet-of-Things sensors and new NASA and international observatories being planned, these incredible amounts of data will soon be augmented by even larger amounts of diverse data and therefore will become more and more difficult to access, integrate, understand and utilize. At the same time, because of climate change and its impacts, in order to predict, manage, and mitigate the effects of extreme science events and disasters, the information produced by all of this data needs to be optimized, organized and analyzed in such a way that it can be utilized by traditional as well as many new non-traditional users. This calls for the development of observing systems that are agile, coordinated and responsive to events of interest, and for information systems to be dynamic, interactive and fast. With these objectives in mind, the Advanced Information Systems Technology (AIST) Program has been developing technologies that will allow for the development of Novel Observing Strategies (NOS) in a distributed, coordinated fashion (i.e., similar to an “Internet-of-Earth-Things”), as well as for the development of novel information systems or Earth System Digital Twins (ESDT) that will build a digital replica of the past and current states of the Earth systems, will derive forecasts of future states under nominal assumptions and will also offer the capability to investigate many hypothetical evolution scenarios under varying impact assumptions. Both NOS and ESDT will be facilitated by the unprecedented advances of Artificial Intelligence technologies, especially Machine Learning (ML), for extracting relevant information from these large amounts of data, for fusing and assimilating diverse data together and for running complex models faster. Together NOS and ESDT will help build the capabilities needed to anticipate, predict and mitigate the effects of future disasters."

Earth Science Remote Sensing; Information Systems↗