Search NASA⌕ Search

SEARCH · Search NASA

Results for “knowledge extraction”

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.

At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
NASA NTRS2014

The Lunar Magma Ocean: Sharpening the Focus on Process and Composition

The currently accepted model for the formation of the lunar anorthositic crust is by flotation from a crystallizing lunar magma ocean (LMO) shortly following lunar accretion. Anorthositic crust is globally distributed and old, whereas the mare basalts are younger and derived from a source region that has experienced plagioclase extraction. Several attempts at modelling such a crystallization sequence have been made [e.g. 1, 2], but our ever-increasing knowledge of the lunar samples and surface have raised as many questions as these models have answered. This abstract presents results from our ongoing ex-periments simulating LMO crystallization and address-ing a range of variables. We investigate two bulk com-positions, which span most of the range of suggested lunar bulk compositions, from the refractory element enriched Taylor Whole Moon (TWM) [3] to the more Earth-like Lunar Primitive Upper Mantle (LPUM) [4]. We also investigate two potential crystallization mod-els: Fully fractional, where crystallizing phases are separated from the magma as they form and sink (or float in the case of plagioclase) throughout magma ocean solidification; and a two-step process suggested by [1, 5] with an initial stage of equilibrium crystalliza-tion, where crystals remain entrained in the magma before the crystal burden increases viscosity enough that convection slows and the crystals settle, followed by fractional crystallization. Here we consider the frac-tional crystallization part of this process; the equilibri-um cumulates having been determined by [6].

Rapp, J. F.↗
NASA NTRS1997

Monaural Sound Localization Revisited

Research reported during the past few decades has revealed the importance for human sound localization of the so-called 'monaural spectral cues.' These cues are the result of the direction-dependent filtering of incoming sound waves accomplished by the pinnae. One point of view about how these cues are extracted places great emphasis on the spectrum of the received sound at each ear individually. This leads to the suggestion that an effective way of studying the influence of these cues is to measure the ability of listeners to localize sounds when one of their ears is plugged. Numerous studies have appeared using this monaural localization paradigm. Three experiments are described here which are intended to clarify the results of the previous monaural localization studies and provide new data on how monaural spectral cues might be processed. Virtual sound sources are used in the experiments in order to manipulate and control the stimuli independently at the two ears. Two of the experiments deal with the consequences of the incomplete monauralization that may have contaminated previous work. The results suggest that even very low sound levels in the occluded ear provide access to interaural localization cues. The presence of these cues complicates the interpretation of the results of nominally monaural localization studies. The third experiment concerns the role of prior knowledge of the source spectrum, which is required if monaural cues are to be useful. The results of this last experiment demonstrate that extraction of monaural spectral cues can be severely disrupted by trial-to-trial fluctuations in the source spectrum. The general conclusion of the experiments is that, while monaural spectral cues are important, the monaural localization paradigm may not be the most appropriate way to study their role.

Wightman, Frederic L.↗
DOE OSTI2024

Multidimensional perspectives of geo-epidemiology: from interdisciplinary learning and research to cost–benefit oriented decision-making

Research typically promotes two types of outcomes (inventions and discoveries), which induce a virtuous cycle: something suspected or desired (not previously demonstrated) may become known or feasible once a new tool or procedure is invented and, later, the use of this invention may discover new knowledge. Research also promotes the opposite sequence—from new knowledge to new inventions. This bidirectional process is observed in geo-referenced epidemiology—a field that relates to but may also differ from spatial epidemiology. Geo-epidemiology encompasses several theories and technologies that promote inter/transdisciplinary knowledge integration, education, and research in population health. Based on visual examples derived from geo-referenced studies on epidemics and epizootics, this report demonstrates that this field may extract more (geographically related) information than simple spatial analyses, which then supports more effective and/or less costly interventions. Actual (not simulated) bio-geo-temporal interactions (never captured before the emergence of technologies that analyze geo-referenced data, such as geographical information systems) can now address research questions that relate to several fields, such as Network Theory. Thus, a new opportunity arises before us, which exceeds research: it also demands knowledge integration across disciplines as well as novel educational programs which, to be biomedically and socially justified, should demonstrate cost-effectiveness. Grounded on many bio-temporal-georeferenced examples, this report reviews the literature that supports this hypothesis: novel educational programs that focus on geo-referenced epidemic data may help generate cost-effective policies that prevent or control disease dissemination.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

LUCID Thrust 1 - Dataset Identification and Biodata Catalog Creation

The LUCID DOE consortium, part of the Department of Energy’s Biological and Environmental Research (BER) program, advances Low Dose Radiation (LDR) research through multidisciplinary efforts across seven key thrusts. This document focuses on Thrust 1, which centers on the creation of curated multimodal population health datasets and supports broader efforts within the LUCID program, including AI-based hypothesis generation, experimental design, and the study of LDR-induced health risks. Specifically, it describes the identification and cataloging of Thrust 1’s curated LDR datasets and biodata, emphasizing their critical role in supporting various research thrusts within the consortium, with potential applications in healthcare and public policy. In addition, the document includes an evaluation of three Large Language Models (LLMs)—GPT-4, SOLAR-10B, and Mixtral-8x7B—based on their ability to extract features from 25 LDR studies. The results indicate that GPT-4 performed the best, while Mixtral-8x7B demonstrated limited knowledge. Overall, this work advances understanding in radiation protection, risk assessment, and medical treatments, while providing valuable resources for researchers, educators, and policymakers.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2025

Rapid Adaptation of Chemical Named Entity Recognition Using Few-Shot Learning and LLM Distillation

Named entity recognition (NER) has been widely used in chemical text mining for the automatic identification and extraction of chemical entities. However, existing chemical NER systems primarily focus on scenarios with abundant training data, requiring significant human effort on annotations. This poses challenges for applications in the chemical field, such as catalysis, where many advancements have traditionally relied on trial-and-error investigations and incremental adjustment of variables. This hinders catalysis science and technology progress in addressing emerging energy and environmental crises. In this work, we propose a few-shot NER model that can quickly adapt to extract new types of chemical entities by using only a limited number of annotated examples. Our model employs a metric-learning approach to transfer entity similarity knowledge from high-resource chemical domains (with abundant annotations) to enable effective entity recognition in low-resource specialized domains (limited annotation). We validate the effectiveness of our model on a few-shot chemical NER benchmark built based on six existing chemical NER data sets. Experiments show that the proposed few-shot NER model can achieve reasonable performance with only 5 examples per entity type and shows consistent improvement as the number of examples increases. Furthermore, we demonstrate how the proposed model can be trained with large language model (LLM) annotated data, opening a new pathway for rapid adaptation of NER systems. Furthermore, our approach leverages the knowledge broadness of large language models for chemistry while distilling this knowledge into a lightweight model suitable for efficient and in-house use.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2002

Topology for efficient information dissemination in ad-hoc networking

In this paper, we explore the information dissemination problem in ad-hoc wirless networks. First, we analyze the probability of successful broadcast, assuming: the nodes are uniformly distributed, the available area has a lower bould relative to the total number of nodes, and there is zero knowledge of the overall topology of the network. By showing that the probability of such events is small, we are motivated to extract good graph topologies to minimize the overall transmissions. Three algorithms are used to generate topologies of the network with guaranteed connectivity. These are the minimum radius graph, the relative neighborhood graph and the minimum spanning tree. Our simulation shows that the relative neighborhood graph has certain good graph properties, which makes it suitable for efficient information dissemination.

network topology↗
NASA NTRS2015

Lunar Magma Ocean Crystallization: Constraints from Fractional Crystallization Experiments

The currently accepted paradigm of lunar formation is that of accretion from the ejecta of a giant impact, followed by crystallization of a global scale magma ocean. This model accounts for the formation of the anorthosite highlands crust, which is globally distributed and old, and the formation of the younger mare basalts which are derived from a source region that has experienced plagioclase extraction. Several attempts at modelling the crystallization of such a lunar magma ocean (LMO) have been made, but our ever-increasing knowledge of the lunar samples and surface have raised as many questions as these models have answered. Geodynamic models of lunar accretion suggest that shortly following accretion the bulk of the lunar mass was hot, likely at least above the solidus]. Models of LMO crystallization that assume a deep magma ocean are therefore geodynamically favorable, but they have been difficult to reconcile with a thick plagioclase-rich crust. A refractory element enriched bulk composition, a shallow magma ocean, or a combination of the two have been suggested as a way to produce enough plagioclase to account for the assumed thickness of the crust. Recently however, geophysical data from the GRAIL mission have indicated that the lunar anorthositic crust is not as thick as was initially estimated, which allows for both a deeper magma ocean and a bulk composition more similar to the terrestrial upper mantle. We report on experimental simulations of the fractional crystallization of a deep (approximately 100km) LMO with a terrestrial upper mantle-like (LPUM) bulk composition. Our experimental results will help to define the composition of the lunar crust and mantle cumulates, and allow us to consider important questions such as source regions of the mare basalts and Mg-suite, the role of mantle overturn after magma ocean crystallization and the nature of KREEP

Rapp, J. F.↗
NASA NTRS1975

IMAGE 100: The interactive multispectral image processing system

The need for rapid, cost-effective extraction of useful information from vast quantities of multispectral imagery available from aircraft or spacecraft has resulted in the design, implementation and application of a state-of-the-art processing system known as IMAGE 100. Operating on the general principle that all objects or materials possess unique spectral characteristics or signatures, the system uses this signature uniqueness to identify similar features in an image by simultaneously analyzing signatures in multiple frequency bands. Pseudo-colors, or themes, are assigned to features having identical spectral characteristics. These themes are displayed on a color CRT, and may be recorded on tape, film, or other media. The system was designed to incorporate key features such as interactive operation, user-oriented displays and controls, and rapid-response machine processing. Owing to these features, the user can readily control and/or modify the analysis process based on his knowledge of the input imagery. Effective use can be made of conventional photographic interpretation skills and state-of-the-art machine analysis techniques in the extraction of useful information from multispectral imagery. This approach results in highly accurate multitheme classification of imagery in seconds or minutes rather than the hours often involved in processing using other means.

Schaller, E. S.↗
NASA NTRS2010

Human Factors Research for Space Exploration: Measurement, Modeling, and Mitigation

As part of NASA's Human Research Program, the Space Human Factors Engineering Project serves as the bridge between Human Factors research and Human Spaceflight applications. Our goal is to be responsive to the operational community while addressing issues at a sufficient level of abstraction to ensure that our tools and solutions generalize beyond the point design. In this panel, representatives from four of our research domains will discuss the challenges they face in solving current problems while also enabling future capabilities. Historically, engineering-dominated organizations have tended to view good Human Factors (HF) as a desire rather than a requirement in system design and development. Our field has made significant gains in the past decade, however; the Department of Defense, for example, now recognizes Human-System Integration (HSI), of which HF is a component, as an integral part of their divisions hardware acquisition processes. And our own agency was far more accepting of HF/HSI requirements during the most recent vehicle systems definition than in any prior cycle. Nonetheless, HF subject matter experts at NASA often find themselves in catch up mode... coping with legacy systems (hardware and software) and procedures that were designed with little regard for the human element, and too often with an attitude of we can deal with any operator issues during training. Our challenge, then, is to segregate the true knowledge gaps in Space Human Factors from the prior failures to incorporate best (or even good) HF design principles. Further, we strive to extract the overarching core HF issues from the point-design-specific concerns that capture the operators (and managers) attention. Generally, our approach embraces a 3M approach to Human Factors: Measurement, Modeling, and Mitigation. Our first step is to measure human performance, to move from subjective anecdotes to objective, quantified data. Next we model the phenomenon, using appropriate methods in our field, modifying them to suit the unique aspects of the space environment. Finally, we develop technologies, tools, and procedures to mitigate the decrements in human performance and capabilities that occur in space environments. When successful, we decrease risks to crew safety and to mission success. When extremely successful (or lucky), we devise generalizable solutions that advance the state of our practice. Our panel is composed of researchers from diverse domains of our project... from different boxes, if you will, of the Human Factors Analysis and Classification System (HFACS).

Kaiser, Mary K.↗
NASA NTRSDate not supplied

Detecting and Characterizing Patterns of Failure in Complex Engineered Systems: an Ontology Development and Clustering Approach

While the causes of failures in complex engineered systems are often clear in hindsight, it can be challenging to predict failures proactively during the design of novel engineered products or systems. Identifying patterns can be useful for capturing common characteristics that may lead to failure. In this paper, we present a methodology for identifying patterns of failure from NASA’s publicly available Lessons Learned Information System (LLIS). We apply an ontology development and clustering approach to identify representative patterns leading to failures in historical lessons learned. A joint inductive-deductive approach reveals the key themes in lessons that lead to failure, which are formalized and recorded as an ontology of complex systems failure causes. Documents from the LLIS are manually tagged with relevant characteristics from the ontology. From the tagged set, clustering is used to capture co-occurring sets of characteristics that lead to failure. The primary contribution of this work is a method for extracting a set of generic failure patterns in complex engineered systems and characteristics for these patterns that can be identified at design time, knowledge of which can be used to plan mitigation strategies.

Systems Engineering↗
NASA NTRSDate not supplied

Detecting and Characterizing Patterns of Failure in Complex Systems: An Ontology Development and Clustering Approach

While the causes of failures in complex engineered systems are often clear in hindsight, it can be challenging to predict failures proactively during the design of novel engineered products or systems. Identifying patterns can be useful for capturing common characteristics that may lead to failure. In this paper, we present a methodology for identifying patterns of failure from NASA’s publicly available Lessons Learned Information System (LLIS). We apply an ontology development and clustering approach to identify representative patterns leading to failures in historical lessons learned. A joint inductive-deductive approach reveals the key themes in lessons that lead to failure, which are formalized and recorded as an ontology of complex systems failure causes. Documents from the LLIS are manually tagged with relevant characteristics from the ontology. From the tagged set, clustering is used to capture co-occurring sets of characteristics that lead to failure. The primary contribution of this work is a method for extracting a set of generic failure patterns in complex engineered systems and characteristics for these patterns that can be identified at design time, knowledge of which can be used to plan mitigation strategies.

Systems Engineering↗
NASA NTRS1988

Intelligent data reduction for autonomous power systems

Since 1984 Marshall Space Flight Center was actively engaged in research and development concerning autonomous power systems. Much of the work in this domain has dealt with the development and application of knowledge-based or expert systems to perform tasks previously accomplished only through intensive human involvement. One such task is the health status monitoring of electrical power systems. Such monitoring is a manpower intensive task which is vital to mission success. The Hubble Space Telescope testbed and its associated Nickel Cadmium Battery Expert System (NICBES) were designated as the system on which the initial proof of concept for intelligent power system monitoing will be established. The key function performed by an engineer engaged in system monitoring is to analyze the raw telemetry data and identify from the whole only those elements which can be considered significant. This function requires engineering expertise on the functionality of the system, the mode of operation and the efficient and effective reading of the telemetry data. Application of this expertise to extract the significant components of the data is referred to as data reduction. Such a function possesses characteristics which make it a prime candidate for the application of knowledge-based systems' technologies. Such applications are investigated and recommendations are offered for the development of intelligent data reduction systems.

Floyd, Stephen A.↗
NASA NTRS1985

Cometary comae

The basis of our knowledge about the structure and composition of a comet nucleus is examined. Such knowledge is inferred from observations of the cometary atmosphere (coma). However, photodissociation, photoionization, and chemistry destroy the mother molecules evaporating from the nucleus. To extract the primary information, the chemical kinetics and the physics of the coma are modeled with a computer and the results are compared with coma observations. The physics and chemistry for a dust free coma are described taking into account energy balance, multi-fluid flow for fast atomic and molecular hydrogen and the bulk fluid, and the transition from a collision dominated inner region to the free molecular flow outer region. Special attention is paid to the molecular data requirements for the current modeld and for extended models which will include solar wind interaction and dust. Such models are an important tool in support of the Giotto mission to Halley's comet, in the analysis and interpretation of coma observations, and in the understanding of the earliest history of the solar system.

Huebner, W. F.↗
NASA NTRS2006

FuzzObserver

Fuzzy Feature Observation Planner for Small Body Proximity Observations (FuzzObserver) is a developmental computer program, to be used along with other software, for autonomous planning of maneuvers of a spacecraft near an asteroid, comet, or other small astronomical body. Selection of terrain features and estimation of the position of the spacecraft relative to these features is an essential part of such planning. FuzzObserver contributes to the selection and estimation by generating recommendations for spacecraft trajectory adjustments to maintain the spacecraft's ability to observe sufficient terrain features for estimating position. The input to FuzzObserver consists of data from terrain images, including sets of data on features acquired during descent toward, or traversal of, a body of interest. The name of this program reflects its use of fuzzy logic to reason about the terrain features represented by the data and extract corresponding trajectory-adjustment rules. Linguistic fuzzy sets and conditional statements enable fuzzy systems to make decisions based on heuristic rule-based knowledge derived by engineering experts. A major advantage of using fuzzy logic is that it involves simple arithmetic calculations that can be performed rapidly enough to be useful for planning within the short times typically available for spacecraft maneuvers.

Howard, Ayanna↗
NASA NTRS2015

Online Tools for Uncovering Data Quality (DQ) Issues in Satellite-Based Global Precipitation Products

Data quality (DQ) has many attributes or facets (i.e., errors, biases, systematic differences, uncertainties, benchmark, false trends, false alarm ratio, etc.)Sources can be complicated (measurements, environmental conditions, surface types, algorithms, etc.) and difficult to be identified especially for multi-sensor and multi-satellite products with bias correction (TMPA, IMERG, etc.) How to obtain DQ info fast and easily, especially quantified info in ROI Existing parameters (random error), literature, DIY, etc.How to apply the knowledge in research and applications.Here, we focus on online systems for integration of products and parameters, visualization and analysis as well as investigation and extraction of DQ information.

precipitation↗
NASA NTRS2005

Extracting trends from two decades of microgravity macromolecular crystallization history

Since the 1980s hundreds of macromolecular crystal growth experiments have been performed in the reduced acceleration environment of an orbiting spacecraft. Significant enhancements in structural knowledge have resulted from X-ray diffraction of the crystals grown. Similarly, many samples have shown no improvement or degradation in comparison to those grown on the ground. A complex series of interrelated factors affect these experiments and by building a comprehensive archive of the results it was aimed to identify factors that result in success and those that result in failure. Specifically, it was found that dedicated microgravity missions increase the chance of success when compared with those where crystallization took place as a parasitic aspect of the mission. It was also found that the chance of success could not be predicted based on any discernible property of the macromolecule available to us.

Review↗
NASA NTRS1991

On prediction and discovery of lunar ores

Sampling of lunar material and remote geochemical, mineralogical, and photogeologic sensing of the lunar surface, while meager, provide first-cut information about lunar composition and geochemical separation processes. Knowledge of elemental abundances in known lunar materials indicates which common lunar materials might serve as ores if there is economic demand and if economical extraction processes can be developed, remote sensing can be used to extend the understanding of the Moon's major geochemical separations and to locate potential ore bodies. Observed geochemical processes might lead to ores of less abundant elements under extreme local conditions.

Haskin, Larry A.↗