Results for “Knowledge” — Page 19

NASA NTRS2018

Application of a Knowledge-Based Optimization Method for Aerodynamic Design

The current research is investigating the application of an optimization technique to an existing knowledge-based design tool. The optimization method, referred to as CODISC, helps improve the results from a knowledge-based design by eliminating the required advanced design knowledge, or help fine-tune a well-performing vehicle. Three CODISC designs are presented using a generic transonic transport, the Common Research Model (CRM). One design optimizes the baseline CRM to demonstrate the ability to improve a well-performing vehicle. Another design is performed from the CRM with camber and twist removed, which highlights the ability to use CODISC in the conceptual design phase. The final design implements laminar flow on the CRM, showing how CODISC can optimize the extent of laminar flow to find the best aerodynamic performance. All three CODISC designs reduced the vehicle drag compared to the baseline CRM, and highlight the new optimization technique’s versatility in the aircraft design industry.

CODISC↗

NASA NTRSDate not supplied

Modeling Atmospheric Science Knowledge from Research Publications

NASA Earth Science Data Centers contain enormous amounts of remote sensing digital data. It is often a significant challenge for users to find data suitable for their research topic in these vast archives. One of the approaches is the usage-driven dataset discovery, where users seek publications on projects similar to their intended study. For this approach to be effective, users need a clear connection between the underlying data in the publications and the study objectives; this is not often apparent to non-expert users. Tools and methodologies that can help facilitate and organize these connections are therefore valuable for creating improved knowledge mappings, which can be further used by search engines to suggest data or publications best tailored to a user’s specific research goal. As an illustration of these challenges, in this work we focus on the atmospheric chemistry processes related to Earth environmental impacts such as ozone depletion, aerosols, smog formation, acid rain, and radiative forcing. We further limit our study to publications that use data from the Microwave Limb Sounder (MLS) instrument flown on the Aura Earth Observing System. To create knowledge representations of science carried out in these publications, we use existing ontologies such as the Global Change Master Directory (GCMD) and Semantic Web for Earth and Environmental Terminology (SWEET). These ontologies together encompass term dictionaries that include measured variables, names of molecules or radicals, mission and instrument names, locations, action words, among many others. Based on these terms acknowledge graph database was populated with the terms retrieved from scientific publications that study atmospheric chemistry. These databases can be used to further enhance the automation of knowledge discovery and facilitate machine learning and artificial intelligence algorithms or applications. These tools and methods can also be extended to apply to content from other related Earth science domains.

Irina Gerasimov↗

NASA NTRSDate not supplied

Verb Sense Disambiguation for Densifying Knowledge Graphs in Earth Science

We begin with an ambitious goal: to create a knowledge graph that spans the entire discipline of Earth science. In order to achieve this, we need to apply Natural Language Processing (NLP) techniques on Earth science journal articles to extract their semantic components for the graph. When sentences from Earth science journal articles are broken down into their semantic components and loaded onto a graph, the relationships among these semantic components are represented by the verbs in the sentences. However, since there are multiple verbs in English that can be used to denote the same meaning, the knowledge graph can become sparse and so can the results when we query the graph. In order to ensure quality results, it would be desirable to consolidate similar verbs into a single "class". So, this is the problem at hand: how do we make sure that multiple verbs that mean the same thing are represented as a single class of verb in the knowledge graph? Or in other words, how do we distinguish which meaning a particular verb takes given a particular sentence? In this poster, we demonstrate a potential technique to solve this problem.

Ashish Acharya↗

NASA NTRSDate not supplied

ISS External Microorganisms: A Payload to Close Planetary Protection Knowledge Gaps for Crewed Missions

Before NASA or COSPAR is able to set planetary protection requirements for crewed missions to locations like Mars there are a number of critical knowledge gaps that must be addressed (1). One of the most important knowledge gaps is an understanding of microbial leakage from crewed habitats and space suits. Current ECLSS (Environmental Control and Life Support System) and PLSS (Portable Life Support System) requirements do not include any provisions to control microbes that may escape along with vented or leaked gasses. The current generation of NASA space suits can leak at rates as high as 100 cm2 /min. during nominal operation (2). ISS (International Space Station) intentionally vents atmospheric gases like CO2 to maintain habitable conditions for the crew. Furthermore, every time an airlock is used for EVA (extravehicular activity)there is an accompanying release of internal atmosphere. Since it is not possible to sterilize a crewed mission, it is important that we understand what if any microbes are entrained in these vented and leaked products. It is also important to understand if these microbes can survive on exterior surfaces. Recent sampling of the Russian segments of ISS suggest that bacteria and fungi from inside ISS may be capable of surviving on external surfaces(3). NASA is developing an aseptic sampling tool for use during EVA and plans to collect samples from vents on ISS to build on these results. The results of this work will be used to develop planetary protection requirements for vented and leaked gasses from crewed volumes. NASA has developed and tested a tool kit for collecting microbiological samples during EVA(4). This tool kit contains eight commercially available, 23 mm. diameter, foam swabs that can be used to aseptically collect samples while at vacuum. The swabs are individually housed in aluminum canisters that are equipped with 0.2 μm Teflon filters. These filters allow the canisters to equilibrate to pressure changes while preventing microbiological contamination. The canisters will be cleaned and sterilized before flight. Results from ground-based testing indicate that this tool kit is capable of aseptically collecting microbes while at vacuum without becoming contaminated during pressure changes(5). Based on the results of this ground testing we have modified the tool kit to meet NASA safety requirements and improve the ergonomics. We added additional mounting points to the tool kit to give astronauts more options for securing it during use. We also changed the opening mechanism to improve the precision with which swabs can be extracted from the tool kit. We plan to use this kit on an upcoming EVA to collect samples from non-propulsive vents and areas near the U.S. airlock on ISS. These samples will be frozen at -80 ̊C and stored on station until they can be returned to Earth. We will analyze these returned samples using next generation DNA sequencing to determine the community composition and function of external ISS environments. The results of this study will close planetary protection knowledge gaps for crewed missions and will help NASA determine appropriate planetary protection requirements for life support systems. The tool kit will also be useful for collecting aseptic samples on upcoming crewed or robotic missions and could easily be modified to collect samples with organic contamination control requirements as well.

A B Regberg↗

NASA NTRSDate not supplied

ISS External Microorganisms: A Payload to Close Planetary Protection Knowledge Gaps for Crewed Missions

Before NASA or COSPAR is able to set planetary protection requirements for crewed missions to locations like Mars there are a number of critical knowledge gaps that must be addressed (1). One of the most important knowledge gaps is an understanding of microbial leakage from crewed habitats and space suits. Current ECLSS (Environmental Control and Life Support System) and PLSS (Portable Life Support System) requirements do not include any provisions to control microbes that may escape along with vented or leaked gasses. The current generation of NASA space suits can leak at rates as high as 100 cm2 /min. during nominal operation (2). ISS (International Space Station) intentionally vents atmospheric gases like CO2 to maintain habitable conditions for the crew. Furthermore, every time an airlock is used for EVA (extravehicular activity)there is an accompanying release of internal atmosphere. Since it is not possible to sterilize a crewed mission, it is important that we understand what if any microbes are entrained in these vented and leaked products. It is also important to understand if these microbes can survive on exterior surfaces. Recent sampling of the Russian segments of ISS suggest that bacteria and fungi from inside ISS may be capable of surviving on external surfaces(3). NASA is developing an aseptic sampling tool for use during EVA and plans to collect samples from vents on ISS to build on these results. The results of this work will be used to develop planetary protection requirements for vented and leaked gasses from crewed volumes. NASA has developed and tested a tool kit for collecting microbiological samples during EVA(4). This tool kit contains eight commercially available, 23 mm. diameter, foam swabs that can be used to aseptically collect samples while at vacuum. The swabs are individually housed in aluminum canisters that are equipped with 0.2 μm Teflon filters. These filters allow the canisters to equilibrate to pressure changes while preventing microbiological contamination. The canisters will be cleaned and sterilized before flight. Results from ground-based testing indicate that this tool kit is capable of aseptically collecting microbes while at vacuum without becoming contaminated during pressure changes(5). Based on the results of this ground testing we have modified the tool kit to meet NASA safety requirements and improve the ergonomics. We added additional mounting points to the tool kit to give astronauts more options for securing it during use. We also changed the opening mechanism to improve the precision with which swabs can be extracted from the tool kit. We plan to use this kit on an upcoming EVA to collect samples from non-propulsive vents and areas near the U.S. airlock on ISS. These samples will be frozen at -80 ̊C and stored on station until they can be returned to Earth. We will analyze these returned samples using next generation DNA sequencing to determine the community composition and function of external ISS environments. The results of this study will close planetary protection knowledge gaps for crewed missions and will help NASA determine appropriate planetary protection requirements for life support systems. The tool kit will also be useful for collecting aseptic samples on upcoming crewed or robotic missions and could easily be modified to collect samples with organic contamination control requirements as well.

A B Regberg↗

NASA NTRS2022

Natural Language Processing Techniques for Intelligent Knowledge Management of Safety Reports

Safety, failure, and incident reports are common artifacts across various domains, including aviation and wildfire response. These reports are often mandatory to submit, resulting in the culmination of large repositories of text-based documents. Simultaneously, these reports and corresponding repositories are often only manually analyzed and queried by users via out-of-date search engines. As a consequence, we have been developing the Manager for Intelligent Knowledge Access (MIKA) toolkit, which uses natural language processing to improve information access and reuse. In this presentation, we discuss natural language processing techniques for knowledge discovery and apply these methods to a repository of aerial wildfire mishap reports. Two methods are used for knowledge discovery: topic modeling and named-entity recognition. We use topic modeling to identify hazards and perform a trend analysis to produce a data-driven risk matrix. A custom named-entity recognition model, build from fine tuning a pre-trained language model, is used to identify failure modes, failure causes, failure effects, control processes, and recommendations to aid in failure modes and effects analysis (FMEA). Throughout the presentation, we discuss and apply natural language processing techniques to better leverage the vast amount of information contained in report repositories.

Machine learning↗

NASA NTRSDate not supplied

Consequences of Asteroid Characterization on the State of Knowledge about Inferred Physical Properties and Impact Risk

Physical characteristics of Near-Earth Objects (NEOs) are essential inputs to planetary defense assessments. The size, density, and strength of an NEO are critical inputs to modeling behavior during atmospheric entry as well as assessing the risk of impact. Similarly, knowledge of the physical characteristics of an object are necessary to evaluate the probable result of a mitigation mission. Usually, these attributes cannot be directly measured, but increasingly sophisticated methods have been developed to infer physical properties from related measurements of asteroids, meteors, and/or meteorites. Fortuitously, some of these measurements have been obtained for enough NEOs to elucidate the distribution of values across the sampled population. However, the situation becomes more challenging when considering a specific asteroid, since it is unlikely that all the relevant measurements have been made for any given object. We have developed a Bayesian network that can combine available information about a particular NEO with knowledge of the larger population to infer probabilistic values and uncertainties for physical characteristics of interest. Distributions of asteroid population albedos, taxonomic classes, and macroporosities, along with meteorite density distributions and associations between taxonomic classes and meteorite classes, provide the default distributions for the network’s parameter nodes. The inference network links parameters for each virtual asteroid either deterministically or probabilistically as appropriate, and eliminates any unphysical combinations of parameters. Within the context of planetary defense, our Bayesian network can be used to constrain the ranges of likely impactor properties, which can subsequently reduce the uncertainty in modelling of atmospheric entry, mitigation efficacy, and impact risk assessment. When additional measurements become available for a specific object, the network incorporates those measurements to generate virtual asteroids with property distributions that are consistent with the measurements. We will use the 2023 PDC scenario to demonstrate how the inference network can be combined with plausible characterization measurements to refine the state of knowledge about likely combinations of physical parameters and the resulting impact risk.

risk assessment↗

NASA NTRSDate not supplied

Consequences of Asteroid Characterization on the State of Knowledge about Inferred Physical Properties and Impact Risk

Physical characteristics of Near-Earth Objects (NEOs) are essential inputs to planetary defense assessments. The size, density, and strength of an NEO are critical inputs to modeling behavior during atmospheric entry as well as assessing the risk of impact. Similarly, knowledge of the physical characteristics of an object are necessary to evaluate the probable result of a mitigation mission. Usually, these attributes cannot be directly measured, but increasingly sophisticated methods have been developed to infer physical properties from related measurements of asteroids, meteors, and/or meteorites. Fortuitously, some of these measurements have been obtained for enough NEOs to elucidate the distribution of values across the sampled population. However, the situation becomes more challenging when considering a specific asteroid, since it is unlikely that all the relevant measurements have been made for any given object. We have developed a Bayesian network that can combine available information about a particular NEO with knowledge of the larger population to infer probabilistic values and uncertainties for physical characteristics of interest. Distributions of asteroid population albedos, taxonomic classes, and macroporosities, along with meteorite density distributions and associations between taxonomic classes and meteorite classes, provide the default distributions for the network’s parameter nodes. The inference network links parameters for each virtual asteroid either deterministically or probabilistically as appropriate, and eliminates any unphysical combinations of parameters. Within the context of planetary defense, our Bayesian network can be used to constrain the ranges of likely impactor properties, which can subsequently reduce the uncertainty in modelling of atmospheric entry, mitigation efficacy, and impact risk assessment. When additional measurements become available for a specific object, the network incorporates those measurements to generate virtual asteroids with property distributions that are consistent with the measurements. We will use the 2023 PDC scenario to demonstrate how the inference network can be combined with plausible characterization measurements to refine the state of knowledge about likely combinations of physical parameters and the resulting impact risk.

risk assessment↗

NASA NTRSDate not supplied

Final Report of the COSPAR Meeting Series on Knowledge Gaps in Planetary Protection for Crewed Missions to Mars

The international scientific community has, through a Committee on Space Research (COSPAR) meeting series, participated in a seven-year review of knowledge gaps in planetary protection for crewed missions to Mars. This meeting series first identified a prioritized list of knowledge gaps that, if addressed, would permit development of an implementable planetary protection policy for the initial crewed Mars mission. Next, a consideration of the available venues (ground, LEO, deep space, planetary surfaces) yielded a roadmap of potential future investigations needed to close the knowledge gaps. Finally, the meeting series evaluated topic-specific measurements that are essential to establishing the figures of merit needed for hardware engineering design requirements. The three main topic threads considered within the meeting series were: microbial and human health monitoring; natural transport of terrestrial biological contamination on Mars, and; technologies and operations for contamination control (of spacecraft systems). Participants at the meeting series included engineers, scientists, managers and various subject matter experts from space agencies, academic and research institutions, commercial companies, and other government departments. This paper provides a detailed summary review of the findings of the meeting series, including descriptions of the basis for each of the figures of merit (where they were identified), and considerations of how planetary protection implementation and policy for crewed missions may progress from the current state of the art.

J Andy Spry↗

DOE OSTI2024

Model-Based Approaches to Generate Knowledge from Data in a Plant Reliability Context

One challenge that nuclear power plant system engineers are facing is that the amount of equipment reliability (ER) data being continuously generated are extremely large. These data elements come in different forms: textual (e.g., condition reports) and numeric (e.g., generated by monitoring systems) and they provide system engineers with valuable insights and information regarding the discovery of anomalous behaviors or degradation trends, the identification of the possible causes behind such behaviors and trends, and the prediction of their direct consequences. This paper directly targets the generation of knowledge from ER data by putting “data into context”. Here, we employ model-based system engineering (MBSE) models of systems and assets to represent and capture their architecture and functional (i.e., cause-effect) relations. ER data elements are processed by identifying first which elements of the developed MBSE elements they are referring to. This task is much harder for textual data since the information contained in issue or maintenance reports needs to “be understood” by a computational tool. Here we called this process “knowledge extraction” where our methods to extract knowledge from textual data. Lastly, once numeric and textual ER data elements have been processed and “understood”, we discover possible cause-effect relations among them. This is performed by observing if a logical connection through the MBSE models exists, and if there is a temporal relation among them. The logic and temporal are the two main ingredients to perform “machine reasoning” from ER data.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Model-Based Approaches to Generate Knowledge from Data in a Plant Reliability Context

One challenge that nuclear power plant system engineers are facing is continuous generation of an extremely large amount of equipment reliability (ER) data. These data elements come in textual (e.g., condition reports) and numeric (e.g., generated by monitoring systems) forms. They provide system engineers with valuable insights and information by discovering anomalous behaviors or degradation trends, identifying possible causes behind such behaviors and trends, and predicting their direct consequences. This paper directly targets the knowledge generation from ER data by putting “data into context.” We employ model-based system engineering (MBSE) of systems and assets to represent and capture their architecture and functional (i.e., cause-effect) relations. ER data elements are processed by first identifying which of the developed MBSE elements they are referring to. This task is harder for textual data since the information contained in issue or maintenance reports needs to be “understood” by a computational tool. We called this process “knowledge extraction” since our methods extract knowledge from textual data. Last, once numeric and textual ER data elements have been processed and “understood,” we discover possible cause-effect relations among them. This is performed by observing whether a logical connection through the MBSE models exists, and if there is a temporal relationship among them. The logic and temporal are the two main ingredients to perform “machine reasoning” from ER data.

97 - MATHEMATICS AND COMPUTING↗

NASA NTRS2003

The power of a question: a case study of two organizational knowledge capture systems

Designers have many options for how to encode knowledge, although most are based on declarative representations. This paper explores the use of questions to represent knowledge.

knowledge management knowledge reuse socratic meth↗

DOE OSTI2025

ARCH: Large-scale knowledge graph via aggregated narrative codified health records analysis

Objective: Electronic health record (EHR) systems contain a wealth of clinical data stored as both codified data and free-text narrative notes (NLP). The complexity of EHR presents challenges in feature representation, information extraction, and uncertainty quantification. Here, to address these challenges, we proposed an efficient Aggregated naRrative Codified Health (ARCH) records analysis to generate a large-scale knowledge graph (KG) for a comprehensive set of EHR codified and narrative features. Methods: Using data from 12.5 million Veterans Affairs patients, ARCH first derives embedding vectors and generates similarities along with associated p-values to measure the strength of relatedness between clinical features with statistical certainty quantification. Next, ARCH performs a sparse embedding regression to remove indirect linkage between features to build a sparse KG. Finally, ARCH was validated on various clinical tasks, including detecting known relationships between entity pairs, predicting drug side effects, disease phenotyping, as well as sub-typing Alzheimer’s disease patients. Results: ARCH produces high-quality clinical embeddings and KG for over 60,000 codified and narrative EHR concepts. The KG and embeddings are visualized in the R-shiny powered web-API.3 ARCH achieved high accuracy in detecting EHR concept relationships, with AUCs of 0.926 (codified) and 0.861 (NLP) for similar EHR concepts, and 0.810 (codified) and 0.843 (NLP) for related pairs. It detected drug side effects with a 0.723 AUC, which improved to 0.826 after fine-tuning. Using both codified and NLP features, the detection power increased significantly. Compared to other methods, ARCH has superior accuracy and enhances weakly supervised phenotyping algorithms’ performance. Notably, it successfully categorized Alzheimer’s patients into two subgroups with varying mortality rates. Conclusion: The proposed ARCH algorithm generates large-scale high-quality semantic representations and knowledge graph for both codified and NLP EHR features, useful for a wide range of predictive modeling tasks.

Electronic health records↗

DOE OSTI2026

Multi‐Decadal Dynamics of Wetland Methane Emissions Revealed by Knowledge‐Guided Machine Learning

Measurement of methane fluxes (FCH 4 ) from natural systems, such as wetlands, has lagged far behind carbon dioxide fluxes. Short and fragmented wetland FCH 4 data limit our ability to assess its long-term dynamics and potential climate feedbacks. Extrapolating short-term FCH 4 records to recent decades remains challenging for both process-based models and data-driven machine learning (ML) approaches. Here, we develop a knowledge-guided ML framework that integrates eddy covariance (EC) FCH 4 observations, field warming experiments, and biogeochemical knowledge to reconstruct the long-term FCH 4 budgets and trends. Focusing on the 11 longest EC monitoring sites in the AmeriFlux network, we found considerable variability in multi-decadal trends of wetland FCH 4 , with increases up to 14% per decade from 2000 to 2024. We also found that the strength of these increasing trends declines from high to low latitudes, highlighting the vulnerability of northern wetlands. This work presents novel and robust reconstructions of long-term wetland FCH 4 , offering critical benchmark datasets for bottom-up ecosystem models and advancing fundamental understanding of wetland biogeochemistry.

AmeriFlux site↗

NASA NTRS1993

Irrelevance Reasoning in Knowledge Based Systems

This dissertation considers the problem of reasoning about irrelevance of knowledge in a principled and efficient manner. Specifically, it is concerned with two key problems: (1) developing algorithms for automatically deciding what parts of a knowledge base are irrelevant to a query and (2) the utility of relevance reasoning. The dissertation describes a novel tool, the query-tree, for reasoning about irrelevance. Based on the query-tree, we develop several algorithms for deciding what formulas are irrelevant to a query. Our general framework sheds new light on the problem of detecting independence of queries from updates. We present new results that significantly extend previous work in this area. The framework also provides a setting in which to investigate the connection between the notion of irrelevance and the creation of abstractions. We propose a new approach to research on reasoning with abstractions, in which we investigate the properties of an abstraction by considering the irrelevance claims on which it is based. We demonstrate the potential of the approach for the cases of abstraction of predicates and projection of predicate arguments. Finally, we describe an application of relevance reasoning to the domain of modeling physical devices.

INFERENCE↗

NASA NTRS2000

Knowledge sharing within organizations: linking art, theory, scenarios and professional experience

In this presentation, Burton and Bailey, discuss the challenges and opportunities in developing knowledge sharing systems in organizations. Bailey provides a tool using imagery and collage for identifying and utilizing the diverse values and beliefs of individuals and groups. Burton reveals findings from a business research study that examines how social construction influences knowledge sharing among task oriented groups.

knowledge management organization theory organizat↗

NASA NTRS1996

Knowledge Acquisition, Validation, and Maintenance in a Planning System for Automated Image Processing

A key obstacle hampering fielding of AI planning applications is the considerable expense of developing, verifying, updating, and maintainting the planning knowledge base (KB). Planning systems must be able to compare favorably in terms of software lifecycle costs to other means of automation such as scripts or rule-based expert systems. This paper describes a planning application of automated imaging processing and our overall approach to knowledge acquisition for this application.

artificial intelligence image processing knowledge↗

NASA NTRS2006

Automated Knowledge Discovery from Simulators

In this paper, we explore one aspect of knowledge discovery from simulators, the landscape characterization problem, where the aim is to identify regions in the input/ parameter/model space that lead to a particular output behavior. Large-scale numerical simulators are in widespread use by scientists and engineers across a range of government agencies, academia, and industry; in many cases, simulators provide the only means to examine processes that are infeasible or impossible to study otherwise. However, the cost of simulation studies can be quite high, both in terms of the time and computational resources required to conduct the trials and the manpower needed to sift through the resulting output. Thus, there is strong motivation to develop automated methods that enable more efficient knowledge extraction.

landscapes↗