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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 NTRS2006

Fluorescent Approaches to High Throughput Crystallography

We have shown that by covalently modifying a subpopulation, less than or equal to 1%, of a macromolecule with a fluorescent probe, the labeled material will add to a growing crystal as a microheterogeneous growth unit. Labeling procedures can be readily incorporated into the final stages of purification, and the presence of the probe at low concentrations does not affect the X-ray data quality or the crystallization behavior. The presence of the trace fluorescent label gives a number of advantages when used with high throughput crystallizations. The covalently attached probe will concentrate in the crystal relative to the solution, and under fluorescent illumination crystals show up as bright objects against a dark background. Non-protein structures, such as salt crystals, will not incorporate the probe and will not show up under fluorescent illumination. Brightly fluorescent crystals are readily found against less bright precipitated phases, which under white light illumination may obscure the crystals. Automated image analysis to find crystals should be greatly facilitated, without having to first define crystallization drop boundaries as the protein or protein structures is all that shows up. Fluorescence intensity is a faster search parameter, whether visually or by automated methods, than looking for crystalline features. We are now testing the use of high fluorescence intensity regions, in the absence of clear crystalline features or "hits", as a means for determining potential lead conditions. A working hypothesis is that kinetics leading to non-structured phases may overwhelm and trap more slowly formed ordered assemblies, which subsequently show up as regions of brighter fluorescence intensity. Preliminary experiments with test proteins have resulted in the extraction of a number of crystallization conditions from screening outcomes based solely on the presence of bright fluorescent regions. Subsequent experiments will test this approach using a wider range of proteins. The trace fluorescently labeled crystals will also emit with sufficient intensity to aid in the automation of crystal alignment using relatively low cost optics, further increasing throughput at synchrotrons.

Pusey, Marc L.↗
NASA NTRS1992

Automated anomaly detection for Orbiter High Temperature Reusable Surface Insulation

The description, analysis, and experimental results of a method for identifying possible defects on High Temperature Reusable Surface Insulation (HRSI) of the Orbiter Thermal Protection System (TPS) is presented. Currently, a visual postflight inspection of Orbiter TPS is conducted to detect and classify defects as part of the Orbiter maintenance flow. The objective of the method is to automate the detection of defects by identifying anomalies between preflight and postflight images of TPS components. The initial version is intended to detect and label gross (greater than 0.1 inches in the smallest dimension) anomalies on HRSI components for subsequent classification by a human inspector. The approach is a modified Golden Template technique where the preflight image of a tile serves as the template against which the postflight image of the tile is compared. Candidate anomalies are selected as a result of the comparison and processed to identify true anomalies. The processing methods are developed and discussed, and the results of testing on actual and simulated tile images are presented. Solutions to the problems of brightness and spatial normalization, timely execution, and minimization of false positives are also discussed.

Cooper, Eric G.↗
NASA NTRS2013

Characterizing Interference in Radio Astronomy Observations through Active and Unsupervised Learning

In the process of observing signals from astronomical sources, radio astronomers must mitigate the effects of manmade radio sources such as cell phones, satellites, aircraft, and observatory equipment. Radio frequency interference (RFI) often occurs as short bursts (< 1 ms) across a broad range of frequencies, and can be confused with signals from sources of interest such as pulsars. With ever-increasing volumes of data being produced by observatories, automated strategies are required to detect, classify, and characterize these short "transient" RFI events. We investigate an active learning approach in which an astronomer labels events that are most confusing to a classifier, minimizing the human effort required for classification. We also explore the use of unsupervised clustering techniques, which automatically group events into classes without user input. We apply these techniques to data from the Parkes Multibeam Pulsar Survey to characterize several million detected RFI events from over a thousand hours of observation.

Doran, G.↗
NASA NTRS2019

Image Labeler: A Web Interface to Catalog Earth Science Events

Advances in machine learning (ML) have made it possible to automatically detect Earth science phenomena from satellite imagery. While useful, ML algorithms typically require an extensive dataset containing labeled images for training. Systematic labeling and management of such datasets is quite cumbersome. With this in mind, we present the Image Labeler. Image Labeler is a fast and scalable cloud-based tool that facilitates the rapid development of Earth science event databases, in order to aid automated ML-based image classification.

Case Study↗
NASA NTRS2013

Characterizing Interference in Radio Astronomy Observations through Active and Unsupervised Learning

In the process of observing signals from astronomical sources, radio astronomers must mitigate the effects of man-made radio sources such as cell phones, satellites, aircraft, and observatory equipment. Radio frequency interference (RFI) often occurs as short bursts (< 1 ms) across a broad range of frequencies, and can be confused with signals from sources of interest such as pulsars. With ever-increasing volumes of data being produced by observatories, automated strategies are required to detect, classify, and characterize these short “transient” RFI events. We investigate an active learning approach in which an astronomer labels events that are most confusing to a classifier, minimizing the human effort required for classification. We also explore the use of unsupervised clustering techniques, which automatically group events into classes without user input. We apply these techniques to data from the Parkes Multibeam Pulsar Survey to characterize several million detected RFI events from over a thousand hours of observation

Doran, G.↗
NASA NTRSDate not supplied

SafeDNN: Understanding and Verifying Neural Networks

The SafeDNN project at NASA Ames explores analysis techniques and tools to ensure that systems that use Deep Neural Networks (DNN) are safe, robust and interpretable. Research directions we are pursuing include: symbolic execution for DNN analysis, label-guided clustering to automatically identify input regions that are robust, parallel and compositional approaches to improve formal SMT-based verification, property inference and automated program repair for DNNs, adversarial training and detection, probabilistic reasoning for DNNs. In this talk I will highlight some of the research advances from SafeDNN, that were already published.

Corina Pasareanu↗
NASA NTRS1972

Automated Microbial Metabolism Laboratory

The Automated Microbial Metabolism Laboratory (AMML) 1971-1972 program involved the investigation of three separate life detection schemes. The first was a continued further development of the labeled release experiment. The possibility of chamber reuse without inbetween sterilization, to provide comparative biochemical information was tested. Findings show that individual substrates or concentrations of antimetabolites may be sequentially added to a single test chamber. The second detection system which was investigated for possible inclusion in the AMML package of assays, was nitrogen fixation as detected by acetylene reduction. Thirdly, a series of preliminary steps were taken to investigate the feasibility of detecting biopolymers in soil. A strategy for the safe return to Earth of a Mars sample prior to manned landings on Mars is outlined. The program assumes that the probability of indigenous life on Mars is unity and then broadly presents the procedures for acquisition and analysis of the Mars sample in a manner to satisfy the scientific community and the public that adequate safeguards are being taken.

Source record↗
NASA NTRS1971

Automated Microbial Metabolism Laboratory

The effect of several environmental parameters on previously developed life detection systems is explored. Initial attempts were made to conduct all the experiments in a moist mode (high soil volume to water volume ratio). However, only labeled release and measurement of ATP were found to be feasible under conditions of low moisture. Therefore, these two life detection experiments were used for most of the environmental effects studies. Three soils, Mojave (California desert), Wyaconda (Maryland, sandy loam) and Victoria Valley (Antarctic desert) were generally used throughout. The environmental conditions studied included: incubation temperature 3 C to 80 C, ultraviolet irradiation of soils, variations in soil/liquid ratio, specific atmospheric gases, various antimetabolites, specific substrates, and variation in pH. An experiment designed to monitor nitrogen metabolism was also investigated.

Source record↗
NASA NTRS1994

Refining fuzzy logic controllers with machine learning

In this paper, we describe the GARIC (Generalized Approximate Reasoning-Based Intelligent Control) architecture, which learns from its past performance and modifies the labels in the fuzzy rules to improve performance. It uses fuzzy reinforcement learning which is a hybrid method of fuzzy logic and reinforcement learning. This technology can simplify and automate the application of fuzzy logic control to a variety of systems. GARIC has been applied in simulation studies of the Space Shuttle rendezvous and docking experiments. It has the potential of being applied in other aerospace systems as well as in consumer products such as appliances, cameras, and cars.

Berenji, Hamid R.↗
NASA NTRS1997

Wings: A New Paradigm in Human-Centered Design

Many aircraft accidents/incidents investigations cite crew error as a causal factor (Boeing Commercial Airplane Group 1996). Human factors experts suggest that crew error has many underlying causes and should be the start of an accident investigation and not the end. One of those causes, the flight deck design, is correctable. If a flight deck design does not accommodate the human's unique abilities and deficits, crew error may simply be the manifestation of this mismatch. Pilots repeatedly report that they are "behind the aircraft" , i.e., they do not know what the automated aircraft is doing or how the aircraft is doing it until after the fact. Billings (1991) promotes the concept of "human-centered automation"; calling on designers to allocate appropriate control and information to the human. However, there is much ambiguity regarding what it mean's to be human-centered. What often are labeled as "human-centered designs" are actually designs where a human factors expert has been involved in the design process or designs where tests have shown that humans can operate them. While such designs may be excellent, they do not represent designs that are systematically produced according to some set of prescribed methods and procedures. This paper describes a design concept, called Wings, that offers a clearer definition for human-centered design. This new design concept is radically different from current design processes in that the design begins with the human and uses the human body as a metaphor for designing the aircraft. This is not because the human is the most important part of the aircraft (certainly the aircraft would be useless without lift and thrust), but because he is the least understood, the least programmable, and one of the more critical elements. The Wings design concept has three properties: a reversal in the design process, from aerodynamics-, structures-, and propulsion-centered to truly human-centered; a design metaphor that guides function allocation and control and display design; and a deliberate distinction between two fundamental functions of design, to complement and to interpret human performance. The complementary function extends the human's capabilities beyond his or her current limitations - this includes sensing, computation, memory, physical force, and human decision making styles and skills. The interpretive (or hermeneutic, Hollnagel 1991) function translates information, functionality, and commands between the human and the aircraft. The Wings design concept allows the human to remain aware of the aircraft through natural interpretation. It also affords great improvements in system performance by maximizing the human's natural abilities and complementing the human's skills in a natural way. This paper will discuss the Wings design concept by describing the reversal in the traditional design process, the function allocation strategy of Wings, and the functions of complementing and interpreting the human.

Schutte, Paul C.↗
NASA NTRS2006

Instruments for Reading Direct-Marked Data-Matrix Symbols

Improved optoelectronic instruments (specially configured digital cameras) for reading direct-marked data-matrix symbols on the surfaces of optically reflective objects (including specularly reflective ones) are undergoing development. Data-matrix symbols are two-dimensional binary patterns that are used, like common bar codes, for automated identification of objects. The first data-matrix symbols were checkerboard-like patterns of black-and-white rectangles, typically existing in the forms of paint, ink, or detachable labels. The major advantage of direct marking (the marks are more durable than are painted or printed symbols or detachable labels) is offset by a major disadvantage (the marks generated by some marking methods do not provide sufficient contrast to be readable by optoelectronic instruments designed to read black-and-white data-matrix symbols). Heretofore, elaborate lighting, lensing, and software schemes have been tried in efforts to solve the contrast problem in direct-mark matrix- symbol readers. In comparison with prior readers based on those schemes, the readers now undergoing development are expected to be more effective while costing less. All of the prior direct-mark matrix-symbol readers are designed to be aimed perpendicularly to marked target surfaces, and they tolerate very little angular offset. However, the reader now undergoing development not only tolerates angular offset but depends on angular offset as a means of obtaining the needed contrast, as described below. The prototype reader (see Figure 1) includes an electronic camera in the form of a charge-coupled-device (CCD) image detector equipped with a telecentric lens. It also includes a source of collimated visible light and a source of collimated infrared light for illuminating a target. The visible and infrared illumination complement each other: the visible illumination is more useful for aiming the reader toward a target, while the infrared illumination is more useful for reading symbols on highly reflective surfaces. By use of beam splitters, the visible and infrared collimated lights are introduced along the optical path of the telecentric lens, so that the target is illuminated and viewed from the same direction.

Schramm, Harry F.↗
NASA NTRSDate not supplied

Using Machine Learning to Infer Pre-Entry Properties for Asteroid Threat Analysis

Accurately assessing asteroid threats relies on knowledge of the asteroid’s pre-entry properties such as size, velocity, and mass. Directly measuring these properties can be infeasible due to the sparsity of events and the accuracy and fidelity of various sensors. Current analysis of an asteroid’s pre-entry properties involves modeling the asteroid’s entry into the Earth’s atmosphere. This process can be time consuming and can require manual adjustment of uncertain modeling specific parameters. NASA Ames has developed a genetic algorithm that can help automate asteroid modeling using the Fragment-Cloud Model (FCM). The algorithm generates realistic energy deposition curves based on actual energy deposition curves from real, observed asteroids. By using these synthetic, labeled energy deposition curves, we developed a one-dimensional convolutional neural network that can predict an asteroid’s pre-entry parameters.

ATAP↗
NASA NTRS1991

Science information systems: Archive, access, and retrieval

The objective of this research is to develop technology for the automated characterization and interactive retrieval and visualization of very large, complex scientific data sets. Technologies will be developed for the following specific areas: (1) rapidly archiving data sets; (2) automatically characterizing and labeling data in near real-time; (3) providing users with the ability to browse contents of databases efficiently and effectively; (4) providing users with the ability to access and retrieve system independent data sets electronically; and (5) automatically alerting scientists to anomalies detected in data.

Campbell, William J.↗
NASA NTRSDate not supplied

LLMs and GenAI Tools to Depict Contributions of Human Systems to Spaceflight Tasks Execution

Recent advancements in Artificial Intelligence and Machine Learning (AI/ML) technologies, particularly Large Language Models (LLMs) capable of sophisticated syntax analysis, offer substantial potential in automating complex processes, thereby saving time and human resources. This study explores the development of an LLM-driven model designed to analyze and categorize a diverse set of Mars mission tasks into 18 predefined Human System Task Categories (HSTCs) based on their textual descriptions. As part of developing the Crew Health and Performance – Probabilistic Risk Assessment (CHP-PRA projects Performance Risk Model (PRisM) proof-of-concept, we established a framework to project performance scores from small-scale tests onto a preliminary list of Mars tasks. The foundation of our model was a comprehensive spreadsheet populated by NASA experts and clinicians, which detailed each Mars task alongside binary indicators of HSTC involvement. This dataset enabled the initial application of supervised ML, training and testing on existing HSTC labels. The HSTCs were originally defined from a medical system perspective, focusing on task impairments due to deteriorated human health. To expand our model's scope to include categories impacting performance, we face the challenge of generating binary labels (0 or 1) for new categories without pre-existing data. We address this by employing Generative AI (GenAI) software to determine whether a given task involved a new category by asking, "Does task A involve using category B?" We validate our approach by comparing the GenAI's binary classifications with the expert-provided labels for existing HSTCs. Notably, we utilize Ollama [4], a locally hosted GenAI tool that does not require cloud access, thus safeguarding NASA's proprietary data from unauthorized exposure. This study demonstrates the feasibility of leveraging cutting-edge AI tools to advance research, paving the way for automation and rapid decision-making in space exploration.

Mona Matar↗
NASA NTRS1983

A survey of automated remote sensing for agriculture

The state-of-the-art of the technology available to make remote sensing crop production estimates is reviewed with reference to several past and present research projects. In particular, attention is given to Landsat data acquisition, registration and preprocessing, data transformation, data modeling, proportion estimation, and labeling. Development stage models and crop condition models are briefly characterized, and areas where further research is needed are identified.

Hall, F. G.↗
NASA NTRS1977

An automated procedure for calculating system matrices from perturbation data generated by an EAI Pacer and 100 hybrid computer system

Techniques are presented for determining the elements of the A, B, C, and D state variable matrices for systems simulated on an EAI Pacer 100 hybrid computer. An automated procedure systematically generates disturbance data necessary to linearize the simulation model and stores these data on a floppy disk. A separate digital program verifies this data, calculates the elements of the system matrices, and prints these matrices appropriately labeled. The partial derivatives forming the elements of the state variable matrices are approximated by finite difference calculations.

Milner, E. J.↗
NASA NTRS2010

Automated, Miniaturized Instrument for Measuring Gene Expression in Space

To facilitate astrobiological studies on the survival and adaptation of microorganisms and mixed microbial cultures to space environment, we have been developing a fully automated, miniaturized system for measuring their gene expression on small spacecraft. This low-cost, multi-purpose instrument represents a major scientific and technological advancement in our ability to study the impact of the space environment on biological systems by providing data on cellular metabolism and regulation orders of magnitude richer than what is currently available. The system supports growth of the organism, lyse it to release the expressed RNA, label the RNA, read the expression levels of a large number of genes by microarray analysis of labeled RNA and transmit the measurements to Earth. To measure gene expression we use microarray technology developed by CombiMatrix, which is based on electrochemical reactions on arrays of electrodes on a semiconductor substrate. Since the electrical integrity of the microarray remains intact after probe synthesis, the circuitry can be employed to sense nucleic acid binding at each electrode. CombiMatrix arrays can be sectored to allow multiple samples per chip. In addition, a single array can be used for several assays. The array has been integrated into an automated microfluidic cartridge that uses flexible reagent blisters and pinch pumping to move liquid reagents between chambers. The proposed instrument will help to understand adaptation of terrestrial life to conditions beyond the planet of origin, identify deleterious effects of the space environment, develop effective countermeasures against these effects, and test our ability to sustain and grow in space organisms that can be used for life support and in situ resource utilization during long-duration space exploration. The instrument is suitable for small satellite platforms, which provide frequent, low cost access to space. It can be also used on any other platform in space, including the ISS. It can be replicated and used with only small modifications in multiple biological experiments with a broad range of goals in mind.

Pohorille, A.↗
NASA NTRS2019

Ask-The-Expert: Minimizing Human Review for Big Data Analytics Through Active Learning

In this CIF project, we worked toward semi-automating knowledge discovery from anomaly detection algorithms through the use of active learning. Active learning is an area of research within machine learning that uses an "expert in the loop" to learn from large data sets that have very few annotations or labels available, and where providing such labels is expensive. In our case, the task can be defined as the identification of safety events from flight operational data. Since traditional anomaly detection algorithms cannot differentiate between operationally relevant and irrelevant statistical anomalies, Subject Matter Experts (SMEs) have a lengthy and expensive burden of investigating every example identified by the detection algorithm, classifying and labeling them as relevant or irrelevant. Active learningidentifies the unlabeled example for which a label would most improve the classifier, asks the domain expert for a label, and repeats this process until there are no more resources (time, budget) available for labeling or a minimum required performance is reached. A positive label indicates an operationally significant safety event whereas a negative label indicates otherwise. Based on these few labels we propose to build an active learning system that utilizes the SME's time in the most effective manner by iteratively asking for labels for as few informative instances as possible. Our work was proposed to be a stepping stone toward implementation and deployment of the system with user interface to be pursued by the Aviation Operations and Safety Program (AOSP) given its interest in safety monitoring and discovery of safety incidents.

aviation safety↗