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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS1990

Technology for an intelligent, free-flying robot for crew and equipment retrieval in space

Crew rescue and equipment retrieval is a Space Station Freedom requirement. During Freedom's lifetime, there is a high probability that a number of objects will accidently become separated. Members of the crew, replacement units, and key tools are examples. Retrieval of these objects within a short time is essential. Systems engineering studies were conducted to identify system requirements and candidate approaches. One such approach, based on a voice-supervised, intelligent, free-flying robot was selected for further analysis. A ground-based technology demonstration, now in its second phase, was designed to provide an integrated robotic hardware and software testbed supporting design of a space-borne system. The ground system, known as the EVA Retriever, is examining the problem of autonomously planning and executing a target rendezvous, grapple, and return to base while avoiding stationary and moving obstacles. The current prototype is an anthropomorphic manipulator unit with dexterous arms and hands attached to a robot body and latched in a manned maneuvering unit. A precision air-bearing floor is used to simulate space. Sensor data include two vision systems and force/proximity/tactile sensors on the hands and arms. Planning for a shuttle file experiment is underway. A set of scenarios and strawman requirements were defined to support conceptual development. Initial design activities are expected to begin in late 1989 with the flight occurring in 1994. The flight hardware and software will be based on lessons learned from both the ground prototype and computer simulations.

Erickson, J. D.↗
NASA NTRSDate not supplied

Developing Open-Source Training Materials for AI/ML and Space Biological Sciences Using NASA Cloud-Based Data

Artificial Intelligence (AI) and Machine Learning (ML) has gained significant traction in the biological and biomedical research fields in the last two decades, in part thanks to an increasing culture of open data sharing and reuse. Due to its capability for identifying complex relationships and patterns, AI/ML methodology is particularly well suited to recognize and predict biological patterns from high-dimensional next-generation sequencing data (e.g. whole genome sequencing, transcriptomic sequencing), as well as from biological or medical imaging data (e.g. microscopy, computed tomography, ultrasound, magnetic resonance imaging, radiography). These methodologies hold particular promise for space biosciences research and automated space health monitoring systems. However, there are many key considerations for properly training, validating, and testing a machine learning model in biological research or clinical application. Even with the positive culture of Open Science and data sharing, inexperienced researchers working quickly without proper checks can produce models that perform poorly outside of the immediate training dataset. Lessons learned from biological AI/ML research indicate that Open Science principles such as data sharing and open-source code must go hand-in-hand with publicly available, high-quality training curricula in best practices, with modules centered on real-life scientific use cases and data so future AI/ML practitioners gain experience on real problems. Here we present the development of open-source training materials for AI/ML and space biosciences, as part of the NASA Transform to Open Science Training (TOPST) initiative. We develop 4 independent training programs, focused on the following topics: 1) Fundamentals of Machine Learning and Space Biosciences Domain, 2) Open Science, Artificial Intelligence, and Ethical Best Practices for Data Sharing and Analysis, 3) Using AI/ML Classification to Identify Gene Networks Affected By Space Exposure in Mouse Liver, and 4) Using Neural Networks to Find DNA Damage Patterns in Immune Cells after Radiation. All programs leverage cloud-based NASA biological datasets. The curriculum we present will enable worldwide access to training in AI/ML and scientific analysis.

James Andrew Casaletto↗
NASA NTRSDate not supplied

Developing Open-Source Training Materials for AI/ML and Space Biological Sciences Using NASA Cloud-Based Data

Artificial Intelligence (AI) and Machine Learning (ML) has gained significant traction in the biological and biomedical research fields, in part due to a culture of open data sharing and reuse. AI/ML methodology is well-suited to recognize and predict biological patterns from high-dimensional next-generation sequencing data (e.g. whole genome sequencing, transcriptomic sequencing), as well as from biological or medical imaging data (e.g. microscopy, computed tomography, ultrasound, magnetic resonance imaging, radiography). These methodologies hold particular promise for space biosciences research and automated space health monitoring systems. However, there are key considerations for properly training, validating, and testing a machine learning model in biological research or clinical application. Inexperienced researchers can produce models that perform poorly outside of the training dataset. Open Science principles such as data sharing and open-source code must go hand-in-hand with publicly available, high-quality training curricula in best practices, with modules centered on real-life scientific use cases and data so future AI/ML practitioners gain experience on real problems. Here we present the development of open-source training materials for AI/ML and space biosciences, as part of the NASA Transform to Open Science Training (TOPST) initiative. We develop 4 independent training programs, focused on the following topics: 1) Fundamentals of Machine Learning and Space Biosciences Domain, 2) Open Science, Artificial Intelligence, and Ethical Best Practices for Data Sharing and Analysis, 3) Using AI/ML Classification to Identify Gene Networks Affected By Space Exposure in Mouse Liver, and 4) Using Neural Networks to Find DNA Damage Patterns in Immune Cells after Radiation. All programs leverage cloud-based NASA biological datasets. The curriculum we present will enable worldwide access to training in AI/ML and scientific analysis.

James Casaletto↗
DOE OSTI2018

Facile carrier-assisted targeted mass spectrometric approach for proteomic analysis of low numbers of mammalian cells

There is an unmet technical challenge for mass spectrometry (MS)-based proteomic analysis of single mammalian cells. Quantitative proteomic analysis of single cells has been previously achieved by antibody-based immunoassays but is limited by the availability of high-quality antibodies. Herein we report a facile targeted MS-based proteomics method, termed cPRISM-SRM (carrier-assisted high-pressure, high-resolution separations with intelligent selection and multiplexing coupled to selected reaction monitoring), for reliable analysis of low numbers of mammalian cells. The method capitalizes on using “carrier protein” to assist processing of low numbers of cells with minimal loss, high-resolution PRISM separation for target peptide enrichment, and sensitive SRM for protein quantification. We have demonstrated that cPRISM-SRM has sufficient sensitivity to quantify proteins expressed at ≥200,000 copies per cell at the single-cell level and ≥3000 copies per cell in 100 mammalian cells. We envision that with further improvement cPRISM-SRM has the potential to move toward targeted MS-based single-cell proteomics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

FRIEDA: Flexible Robust Intelligent Elastic Data Management Framework

Scientific applications are increasingly using cloud resources for their data analysis workflows. However, managing data effectively and efficiently over these cloud resources is challenging due to the myriad storage choices with different performance, cost trade-offs, complex application choices and complexity associated with elasticity, failure rates in these environments. The different data access patterns for data-intensive scientific applications require a more flexible and robust data management solution than the ones currently in existence. FRIEDA is a Flexible Robust Intelligent Elastic Data Management framework that employs a range of data management strategies in cloud environments. FRIEDA can manage storage and data lifecycle of applications in cloud environments. There are four different stages in the data management lifecycle of FRIEDA – (i) storage planning, (ii) provisioning and preparation, (iii) data placement, and (iv) execution. FRIEDA defines a data control plane and an execution plane. The data control plane defines the data partition and distribution strategy, whereas the execution plane manages the execution of the application using a master-worker paradigm. FRIEDA also provides different data management strategies, either to partition the data in real-time, or predetermine the data partitions prior to application execution.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Using Digital Trace Data to Identify Regions and Cities

A greater understanding of human dynamics as they play out in both physical space and through inter-personal communication is vital for the design and development of intelligent and resilient cities. Physical context provides insight in to the space-time distribution of population and their activity patterns, while inter-personal communication can, for the first time, be measured at scale through virtual interactions. The confluence of these two enables spatial social network analysis in the built environment for edge computing, knowledge discovery, and artificial intelligence activities. In this work, we propose a novel method to discover such dynamics. We use a dataset of 72 million tweets to develop a spatially embedded network of communication, and then use community detection algorithms to explore regional and urban delineation in the United States. We find that the broad spatial delineation of communities and community sub-structure is consistent with major United States socio-cultural regions, states, and major metropolitan areas. We describe how these methods could be extended to generate a measure of socially cohesive regions that can be consistently applied anywhere there is a sufficiently rich data source. We hope the proposed methods will generate new perspectives on smart and resilient cities.

Brelsford, Christa↗
NASA NTRS1992

Making intelligent systems team players: Overview for designers

This report is a guide and companion to the NASA Technical Memorandum 104738, 'Making Intelligent Systems Team Players,' Volumes 1 and 2. The first two volumes of this Technical Memorandum provide comprehensive guidance to designers of intelligent systems for real-time fault management of space systems, with the objective of achieving more effective human interaction. This report provides an analysis of the material discussed in the Technical Memorandum. It clarifies what it means for an intelligent system to be a team player, and how such systems are designed. It identifies significant intelligent system design problems and their impacts on reliability and usability. Where common design practice is not effective in solving these problems, we make recommendations for these situations. In this report, we summarize the main points in the Technical Memorandum and identify where to look for further information.

Malin, Jane T.↗
NASA NTRS1995

Applying AI tools to operational space environmental analysis

The U.S. Air Force and National Oceanic Atmospheric Agency (NOAA) space environmental operations centers are facing increasingly complex challenges meeting the needs of their growing user community. These centers provide current space environmental information and short term forecasts of geomagnetic activity. Recent advances in modeling and data access have provided sophisticated tools for making accurate and timely forecasts, but have introduced new problems associated with handling and analyzing large quantities of complex data. AI (Artificial Intelligence) techniques have been considered as potential solutions to some of these problems. Fielding AI systems has proven more difficult than expected, in part because of operational constraints. Using systems which have been demonstrated successfully in the operational environment will provide a basis for a useful data fusion and analysis capability. Our approach uses a general purpose AI system already in operational use within the military intelligence community, called the Temporal Analysis System (TAS). TAS is an operational suite of tools supporting data processing, data visualization, historical analysis, situation assessment and predictive analysis. TAS includes expert system tools to analyze incoming events for indications of particular situations and predicts future activity. The expert system operates on a knowledge base of temporal patterns encoded using a knowledge representation called Temporal Transition Models (TTM's) and an event database maintained by the other TAS tools. The system also includes a robust knowledge acquisition and maintenance tool for creating TTM's using a graphical specification language. The ability to manipulate TTM's in a graphical format gives non-computer specialists an intuitive way of accessing and editing the knowledge base. To support space environmental analyses, we used TAS's ability to define domain specific event analysis abstractions. The prototype system defines events covering reports of natural phenomena such as solar flares, bursts, geomagnetic storms, and five others pertinent to space environmental analysis. With our preliminary event definitions we experimented with TAS's support for temporal pattern analysis using X-ray flare and geomagnetic storm forecasts as case studies. We are currently working on a framework for integrating advanced graphics and space environmental models into this analytical environment.

Krajnak, Mike↗
NASA NTRSDate not supplied

Space Flown Rodent Liver RNA Sequencing Data for Machine Learning in Space Biology Research

High-throughput nucleic acid sequencing (DNA-seq, RNA-seq) has become widespread in biomedical research due to the growing availability and affordability of these assays. Data analysis has been accelerated in recent years by the adoption of artificial intelligence (AI) and machine learning (ML) techniques by biomedical researchers. In space biology research, RNAseq datasets from space-flown experimental samples are critical for characterizing the gene expression aberrations associated with exposure to spaceflight stressors. However, space biological experiments tend to be very low sample size, so identifying proper AI/ML algorithms for sequencing data analysis is an ongoing challenge since these algorithms typically require large sample size. The NASA Science Mission Directorate (SMD) has started the “Benchmark Initiative for AI/ML”, focused on creating datasets meant for three main applications: 1) scientific benchmarking, which finds the best algorithm for a specific problem; 2) application benchmarking, which measures algorithm performance against a set of parameters; and 3) system benchmarking, which evaluates performance of hardware and software architecture. These scientific benchmarks consist of an AI-ready dataset and a reference implementation on a specific scientific question. In this work, we focused on generating standardized datasets to allow the scientific community to benchmark AI/ML algorithms in the domain of space biology. We present here a standardized, AI-ready, publicly available benchmark dataset for space biology RNA-seq data as a collaboration between the NASA AI4LS (Artificial Intelligence for Life Sciences) working group. and NASA’s SMD. This dataset consists of space-flown and ground control mouse liver found in the NASA GeneLab omics database. However, to amplify the small sample number (n=112 samples) for ML purposes, we employ Gaussian noise and a generative adversarial network to extend this dataset to 6,000 synthetic samples, matching the original gene expression characteristics.

James Casaletto↗
NASA NTRS1991

Automated site characterization for robotic sample acquisition systems

A mobile, semi-autonomous vehicle with multiple sensors and on-board intelligence is proposed for performing preliminary scientific investigations on extraterrestrial bodies prior to human exploration. Two technologies, a hybrid optical-digital computer system based on optical correlator technology and an image and instrument data analysis system, provide complementary capabilities which might be part of an instrument package for an intelligent robotic vehicle. The hybrid digital-optical vision system could perform real-time image classification tasks using an optical correlator with programmable matched filters under control of a digital microcomputer. The data analysis system would analyze visible and multiband imagery to extract mineral composition and textural information for geologic characterization. Together these technologies would support the site characterization needs of a robotic vehicle for both navigational and scientific purposes.

Scholl, Marija S.↗
NASA NTRS1990

Artificial intelligence for multi-mission planetary operations

A brief introduction is given to an automated system called the Spacecraft Health Automated Reasoning Prototype (SHARP). SHARP is designed to demonstrate automated health and status analysis for multi-mission spacecraft and ground data systems operations. The SHARP system combines conventional computer science methodologies with artificial intelligence techniques to produce an effective method for detecting and analyzing potential spacecraft and ground systems problems. The system performs real-time analysis of spacecraft and other related telemetry, and is also capable of examining data in historical context. Telecommunications link analysis of the Voyager II spacecraft is the initial focus for evaluation of the prototype in a real-time operations setting during the Voyager spacecraft encounter with Neptune in August, 1989. The preliminary results of the SHARP project and plans for future application of the technology are discussed.

Atkinson, David J.↗
NASA NTRS1980

Analysis of SETI data collected in the parasitic mode

A system for performing SETI observations continuously as part of non-SETI observations at a radio observatory is presented, and the analysis of results obtained by the parasitic system is discussed. The system, designated SERENDIP, is a real-time microprocessor-controlled spectrum analyzer with algorithms for performing statistical operations and recording those spectra with characteristics presumed to be typical of intelligent rather than astrophysical origin. Programs also exist for the post-acquisition analysis of signal autocorrelation, power spectra, time behavior and positional coordinates, and for a generalized cluster analysis to detect clusters of signal detections. In a recent run of 35 days, the SERENDIP system detected 4000 narrowband spectra exceeding a preset threshold, of which 98% were determined to be of instrumental origin. The remaining class of detections is also believed to be instrumental, although not as well organized as the first signals, and means are currently being sought for eliminating them.

Bowyer, S.↗
DOE OSTI2018

A Transparent Translation from Legacy System Model into Common Information Model

Advances in the smart grid are forcing utilities toward better monitoring, control, and analysis of distribution systems, and require extensive cyber-based, intelligent systems and applications to realize various functionalities. The ability of systems, or components within systems, to interact and exchange services or information with each other is the key to the success of smart grid technologies, and it requires efficient information exchanging and data sharing infrastructure. The Common Information Model (CIM) is a standard that allows different applications to exchange information about an electrical system, and it has become a widely accepted solution for information exchange among different platforms and applications. However, most existing legacy systems were developed using their own languages rather than CIM. Integrating such legacy systems is a challenge for utilities, and the appropriate use of the integrated legacy systems is even more intricate. Thus, this paper describes an approach and open-source tool developed to translate legacy system models into CIM format. The developed tool was tested for a commercial distribution management system and simulation results have proved its effectiveness.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Air Blast Mesh Sensitivity and Pressure Mapping Study

Nose cone structural and aerodynamic characteristics are essential for intelligent design of aircraft, spacecraft, and ballistic systems. Finite element analysis can be used to help understand the structural integrity and flight characteristics of different nose cones. A mesh sensitivity study was undertaken for a particular nose cone geometry that was used in tests at LANL facilities in order to confirm the integrity of the meshed geometry. A simple cone that best matched closed-form theoretical solutions was modeled, and received good correlation to the theory. Complexity was then added back to the nose cone. Parameters applied to the simple cone were then implemented in the nose cone geometry giving assurance of accuracy after the geometry was changed. Nose cone results averaged 6.3% error for radial displacement when compared with the theoretical. Hoop stress averaged 6.0% error and meridional stress averaged 5.7% error at the finest mesh level. Meshes showed signs of convergence when compared to all three theoretical solutions. Finally, pressure time-history data from LANL computational fluid dynamics simulations was applied to the surface of the final nose cone geometry. The pressure data was interpolated from pressure gauge locations onto nearby meshed elements, which allowed for FEA software to run simulations on the cone with the pressure data as a loading condition. The pressure mapping resulted in the ability to understand the nose cone’s rigid body motion that in turn can inform design of future nose cones.

42 ENGINEERING↗
NASA NTRS1983

The potential of expert systems for remote sensing application

An overview of the status and potential of artificial intelligence-driven expert systems in the role of image data analysis is presented. An expert system is defined and its structure is summarized. Three such systems designed for image interpretation are outlined. The use of an expert system to detect changes on the earth's surface is discussed, and the components of a knowledge-based image interpretation system and their make-up are outlined. An example of how such a system should work for an area in the tropics where deforestation has occurred is presented as a sequence of situation/action decisions.

Mooneyhan, D. W.↗
NASA NTRS1989

Integrating NASA's Land Analysis System (LAS) image processing software with an appropriate Geographic Information System (GIS): A review of candidates in the public domain

A user requirements analysis (URA) was undertaken to determine and appropriate public domain Geographic Information System (GIS) software package for potential integration with NASA's LAS (Land Analysis System) 5.0 image processing system. The necessity for a public domain system was underscored due to the perceived need for source code access and flexibility in tailoring the GIS system to the needs of a heterogenous group of end-users, and to specific constraints imposed by LAS and its user interface, Transportable Applications Executive (TAE). Subsequently, a review was conducted of a variety of public domain GIS candidates, including GRASS 3.0, MOSS, IEMIS, and two university-based packages, IDRISI and KBGIS. The review method was a modified version of the GIS evaluation process, development by the Federal Interagency Coordinating Committee on Digital Cartography. One IEMIS-derivative product, the ALBE (AirLand Battlefield Environment) GIS, emerged as the most promising candidate for integration with LAS. IEMIS (Integrated Emergency Management Information System) was developed by the Federal Emergency Management Agency (FEMA). ALBE GIS is currently under development at the Pacific Northwest Laboratory under contract with the U.S. Army Corps of Engineers' Engineering Topographic Laboratory (ETL). Accordingly, recommendations are offered with respect to a potential LAS/ALBE GIS linkage and with respect to further system enhancements, including coordination with the development of the Spatial Analysis and Modeling System (SAMS) GIS in Goddard's IDM (Intelligent Data Management) developments in Goddard's National Space Science Data Center.

Rochon, Gilbert L.↗
DOE OSTI2018

A Transparent Translation from Legacy System Model into Common Information Model: Preprint

Advance in smart grid is forcing utilities towards better monitoring, control and analysis of distribution systems, and requires extensive cyber-based intelligent systems and applications to realize various functionalities. The ability of systems, or components within systems, to interact and exchange services or information with each other is the key to the success of smart grid technologies, and it requires efficient information exchanging and data sharing infrastructure. The Common Information Model (CIM) is a standard that allows different applications to exchange information about an electrical system, and it has become a widely accepted solution for information exchange among different platforms and applications. However, most existing legacy systems are not developed using CIM, but using their own languages. Integrating such legacy systems is a challenge for utilities, and the appropriate utilization of the integrated legacy systems is even more intricate. Thus, this paper has developed an approach and open-source tool in order to translate legacy system models into CIM format. The developed tool is tested for a commercial distribution management system and simulation results have proved its effectiveness.

24 POWER TRANSMISSION AND DISTRIBUTION↗