Search NASA⌕ Search

SEARCH · Search NASA

Results for “Knowledge Management”

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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
NASA NTRS1997

Knowledge-Based Aircraft Automation: Managers Guide on the use of Artificial Intelligence for Aircraft Automation and Verification and Validation Approach for a Neural-Based Flight Controller

The ultimate goal of this report was to integrate the powerful tools of artificial intelligence into the traditional process of software development. To maintain the US aerospace competitive advantage, traditional aerospace and software engineers need to more easily incorporate the technology of artificial intelligence into the advanced aerospace systems being designed today. The future goal was to transition artificial intelligence from an emerging technology to a standard technology that is considered early in the life cycle process to develop state-of-the-art aircraft automation systems. This report addressed the future goal in two ways. First, it provided a matrix that identified typical aircraft automation applications conducive to various artificial intelligence methods. The purpose of this matrix was to provide top-level guidance to managers contemplating the possible use of artificial intelligence in the development of aircraft automation. Second, the report provided a methodology to formally evaluate neural networks as part of the traditional process of software development. The matrix was developed by organizing the discipline of artificial intelligence into the following six methods: logical, object representation-based, distributed, uncertainty management, temporal and neurocomputing. Next, a study of existing aircraft automation applications that have been conducive to artificial intelligence implementation resulted in the following five categories: pilot-vehicle interface, system status and diagnosis, situation assessment, automatic flight planning, and aircraft flight control. The resulting matrix provided management guidance to understand artificial intelligence as it applied to aircraft automation. The approach taken to develop a methodology to formally evaluate neural networks as part of the software engineering life cycle was to start with the existing software quality assurance standards and to change these standards to include neural network development. The changes were to include evaluation tools that can be applied to neural networks at each phase of the software engineering life cycle. The result was a formal evaluation approach to increase the product quality of systems that use neural networks for their implementation.

Broderick, Ron↗
NASA NTRS1990

A knowledge base architecture for distributed knowledge agents

A tuple space based object oriented model for knowledge base representation and interpretation is presented. An architecture for managing distributed knowledge agents is then implemented within the model. The general model is based upon a database implementation of a tuple space. Objects are then defined as an additional layer upon the database. The tuple space may or may not be distributed depending upon the database implementation. A language for representing knowledge and inference strategy is defined whose implementation takes advantage of the tuple space. The general model may then be instantiated in many different forms, each of which may be a distinct knowledge agent. Knowledge agents may communicate using tuple space mechanisms as in the LINDA model as well as using more well known message passing mechanisms. An implementation of the model is presented describing strategies used to keep inference tractable without giving up expressivity. An example applied to a power management and distribution network for Space Station Freedom is given.

Riedesel, Joel↗
DOE OSTI2022

NNMC–DP Continuing Education Summary [Slides]

DP technicians voiced a desire for more development and learning opportunities. DP management and technician partners identified the opportunity to expand the knowledge of the technicians through employee development to strengthen the collaboration.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
NASA NTRS1991

Distributed, cooperating knowledge-based systems

Some current research in the development and application of distributed, cooperating knowledge-based systems technology is addressed. The focus of the current research is the spacecraft ground operations environment. The underlying hypothesis is that, because of the increasing size, complexity, and cost of planned systems, conventional procedural approaches to the architecture of automated systems will give way to a more comprehensive knowledge-based approach. A hallmark of these future systems will be the integration of multiple knowledge-based agents which understand the operational goals of the system and cooperate with each other and the humans in the loop to attain the goals. The current work includes the development of a reference model for knowledge-base management, the development of a formal model of cooperating knowledge-based agents, the use of testbed for prototyping and evaluating various knowledge-based concepts, and beginning work on the establishment of an object-oriented model of an intelligent end-to-end (spacecraft to user) system. An introductory discussion of these activities is presented, the major concepts and principles being investigated are highlighted, and their potential use in other application domains is indicated.

Truszkowski, Walt↗
NASA NTRS2003

Using Knowledge-Based Systems to Support Learning of Organizational Knowledge: A Case Study

This paper describes the deployment of a knowledge system to support learning of organizational knowledge at the Jet Propulsion Laboratory (JPL), a US national research laboratory whose mission is planetary exploration and to 'do what no one has done before.' Data collected over 19 weeks of operation were used to assess system performance with respect to design considerations, participation, effectiveness of communication mechanisms, and individual-based learning. These results are discussed in the context of organizational learning research and implications for practice.

knowledge management↗
DOE OSTI2019

FUSRAP Data Management and Information Transitions - 19459

The Formerly Utilized Sites Remedial Action Program (FUSRAP) was created in the mid-1970's to clean up radiological contamination resulting from early development of nuclear weapons. The US Department of Energy (US DOE) was responsible for FUSRAP until October 1997, when Congress transferred administration and execution of FUSRAP site cleanup to the US Army Corps of Engineers (USACE). Currently, the US DOE Office of Legacy Management (LM) determines the eligibility of a site for referral to USACE. USACE determines whether the site is included in FUSRAP for cleanup, conducts remedial actions, and transfers sites back to LM for long-term surveillance and maintenance (LTS and M). LM and USACE established the Data Management Working Group (DMWG) to (1) establish a common understanding of US DOE and USACE district-specific data, records practices, and requirements as they apply to FUSRAP programmatic documents for site transition and transfer; (2) establish methods to ensure accurate data transfer; and (3) develop a timeline for data and records transfer. These methods will ensure continuity of stewardship while transitioning from cleanup to LTS and M and beyond as more FUSRAP sites transfer from USACE to LM. The DMWG has met monthly and the meetings are attended by a wide range of LM and USACE project managers and subject matter experts. Discussion topics have addressed site records management, including data needs and uses; transfer and posting of Administrative Record and Permanent Record files for public accessibility; and management of legal and classified files. There have been a number of group successes, including the following: - Establishment of a common understanding of data and records practices and requirements as the sites transition and transfer from USACE to LM; - Development of effective lines of communication between USACE and LM concerning data management for transition; - Establishment of a cohesive approach to data management between the two agencies. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

KEBLM: Knowledge-Enhanced Biomedical Language Models

Pretrained language models (PLMs) have demonstrated strong performance on many natural language processing (NLP) tasks. Despite their great success, these PLMs are typically pretrained only on unstructured free texts without leveraging existing structured knowledge bases that are readily available for many domains, especially scientific domains. As a result, these PLMs may not achieve satisfactory performance on knowledge-intensive tasks such as biomedical NLP. Comprehending a complex biomedical document without domain-specific knowledge is challenging, even for humans. Inspired by this observation, we propose a general framework for incorporating various types of domain knowledge from multiple sources into biomedical PLMs. We encode domain knowledge using lightweight adapter modules, bottleneck feed-forward networks that are inserted into different locations of a backbone PLM. For each knowledge source of interest, we pretrain an adapter module to capture the knowledge in a self-supervised way. We design a wide range of self-supervised objectives to accommodate diverse types of knowledge, ranging from entity relations to description sentences. Once a set of pretrained adapters is available, we employ fusion layers to combine the knowledge encoded within these adapters for downstream tasks. Each fusion layer is a parameterized mixer of the available trained adapters that can identify and activate the most useful adapters for a given input. Our method diverges from prior work by including a knowledge consolidation phase, during which we teach the fusion layers to effectively combine knowledge from both the original PLM and newly-acquired external knowledge using a large collection of unannotated texts. After the consolidation phase, the complete knowledge-enhanced model can be fine-tuned for any downstream task of interest to achieve optimal performance. Extensive experiments on many biomedical NLP datasets show that our proposed framework consistently improves the performance of the underlying PLMs on various downstream tasks such as natural language inference, question answering, and entity linking. These results demonstrate the benefits of using multiple sources of external knowledge to enhance PLMs and the effectiveness of the framework for incorporating knowledge into PLMs. Finally, while primarily focused on the biomedical domain in this work, our framework is highly adaptable and can be easily applied to other domains, such as the bioenergy sector.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2017

Industrial knowledge design: an approach for designing information artifacts

In this study, the authors define a new approach that addresses the challenge of efficiently designing informational artefacts for optimal knowledge acquisition, an important issue in cognitive ergonomics. Termed Industrial Knowledge Design (or InK'D), it draws from information-related (e.g. informatics) and neurosciences-related (e.g. neuroergonomics) disciplines. Although it can be used for a broad scope of communication-driven business functions, our focus as learning professionals is on conveying knowledge for purposes of training, education, and performance support. This paper discusses preliminary principles of InK'D practice that can be employed to maximise the quality and quantity of transferred knowledge through interaction design. The paper codifies tacit knowledge into explicit concepts that can be leveraged by expert and non-expert knowledge designers alike.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2024

Dynamic Retrieval Augmented Generation of Ontologies using Artificial Intelligence (DRAGON-AI)

Ontologies are fundamental components of informatics infrastructure in domains such as biomedical, environmental, and food sciences, representing consensus knowledge in an accurate and computable form. However, their construction and maintenance demand substantial resources and necessitate substantial collaboration between domain experts, curators, and ontology experts. We present Dynamic Retrieval Augmented Generation of Ontologies using AI (DRAGON-AI), an ontology generation method employing Large Language Models (LLMs) and Retrieval Augmented Generation (RAG). DRAGON-AI can generate textual and logical ontology components, drawing from existing knowledge in multiple ontologies and unstructured text sources.We assessed performance of DRAGON-AI on de novo term construction across ten diverse ontologies, making use of extensive manual evaluation of results. Our method has high precision for relationship generation, but has slightly lower precision than from logic-based reasoning. Our method is also able to generate definitions deemed acceptable by expert evaluators, but these scored worse than human-authored definitions. Notably, evaluators with the highest level of confidence in a domain were better able to discern flaws in AI-generated definitions. We also demonstrated the ability of DRAGON-AI to incorporate natural language instructions in the form of GitHub issues.These findings suggest DRAGON-AI's potential to substantially aid the manual ontology construction process. However, our results also underscore the importance of having expert curators and ontology editors drive the ontology generation process.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2020

Metaevaluation Final Report: Sandia National Laboratories Recorded Information Management Training Program Evaluation

For my internship project, I chose to evaluate the training program for the Sandia National Laboratories (SNL) Recorded Information Management (RIM) Program. The purpose of the evaluation was to identify: Where the current training meets federal and corporate requirements and if not, what gaps exist; If the training is accessible in a consistent location; If the training contains a consistent message about the responsibility of managing documents; If the training is organized by records management subject areas.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2020

49109 UTrain Delta Training

Upon successful completion of this training, the learner will understand how the upgraded interface, functional, and security features of the Laboratory's UTrain Learning Management System impacts administrative users.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2022

NCSP Activities at BNL (FY21) [Slides]

All 3 BNL NCSP (ND, AM, TS) tasks are reported as being on time and slightly under budget. Several COVID-19 related impacts are noted. The release of ENDF/B-VIII.1 has been delayed for 1 year to Feb 2024. New ENDF Library Manager appointment announced as Gustavo Nobre.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2015

Nuclear Energy Knowledge and Validation Center (NEKVaC) Needs Workshop Summary Report

The Department of Energy (DOE) has made significant progress developing simulation tools to predict the behavior of nuclear systems with greater accuracy and of increasing our capability to predict the behavior of these systems outside of the standard range of applications. These analytical tools require a more complex array of validation tests to accurately simulate the physics and multiple length and time scales. Results from modern simulations will allow experiment designers to narrow the range of conditions needed to bound system behavior and to optimize the deployment of instrumentation to limit the breadth and cost of the campaign. Modern validation, verification and uncertainty quantification (VVUQ) techniques enable analysts to extract information from experiments in a systematic manner and provide the users with a quantified uncertainty estimate. Unfortunately, the capability to perform experiments that would enable taking full advantage of the formalisms of these modern codes has progressed relatively little (with some notable exceptions in fuels and thermal-hydraulics); the majority of the experimental data available today is the "historic" data accumulated over the last decades of nuclear systems R&D. A validated code-model is a tool for users. An unvalidated code-model is useful for code developers to gain understanding, publish research results, attract funding, etc. As nuclear analysis codes have become more sophisticated, so have the measurement and validation methods and the challenges that confront them. A successful yet cost-effective validation effort requires expertise possessed only by a few, resources possessed only by the well-capitalized (or a willing collective), and a clear, well-defined objective (validating a code that is developed to satisfy the need(s) of an actual user). To that end, the Idaho National Laboratory established the Nuclear Energy Knowledge and Validation Center to address the challenges of modern code validation and to manage the knowledge from past, current, and future experimental campaigns. By pulling together the best minds involved in code development, experiment design, and validation to establish and disseminate best practices and new techniques, the Nuclear Energy Knowledge and Validation Center (NEKVaC or the ‘Center’) will be a resource for industry, DOE Programs, and academia validation efforts.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2019

DOE Data Day 2019 Report

The Department of Energy (DOE) has joined the larger scientific community in the promotion of data management as a means to higher quality, more efficient research and analysis, and as a critical component of data science. Tools and platforms to support data management and analysis are rapidly evolving and provide enormous opportunities. They also pose challenges that can be specific to DOE but are common across DOE mission areas and organizations. The DOE Data Day (D3) workshop was held on September 25–26, 2019, and organized by a team at Lawrence Livermore National Laboratory (LLNL) in an effort to gather the data management practitioners at the DOE labs together to share their work and results, facilitating knowledge transfers and best practices across project teams. Over 100 attendees participated from multiple national labs—LLNL, Sandia (SNL), Lawrence Berkeley (LBNL), Los Alamos (LANL), Oak Ridge (ORNL), Argonne (ANL), Pacific Northwest (PNNL), Stanford Linear Accelerator (SLAC)—along with Mission Support and Test Services (MSTS), National Energy Technology Laboratory (NETL), Pantex, National Security Complex (Y-12), National Nuclear Security Administration (NNSA), the DOE Office of Scientific and Technical Information (OSTI), and elsewhere in the DOE complex.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2019

Management of Documents and Information on Radioactive Waste - 19455

The assurance of the safety of a repository for low- and intermediate-level radioactive waste, during the 300 years of institutional control after closure, depends on assuming that the responsible institution will be capable to control the documents with information regarding the facility, the disposed of waste and the established measures for counteracting events that could breach the isolation of the radioactive material. Management of documents or information does not represent a major challenge in itself in view of consolidated practices and international standards, such as ISO 15489. However, considering the time scale involved in the disposal of the radioactive wastes, efforts should be made to ensure the preservation and proper access to documents and information. In this matter, the technological aspect of document production can be a complicating factor. In fact, it is common to be unable to access electronic documents produced 20 or 30 years ago, due either to problems related to the media where the information is stored, or to a lack of compatible systems for reading the files. This is just one example of complication when thinking about production, organization, representation, preservation and retrieval of recorded information that is to last for centuries. In this way, this work seeks to present the situation of the management of documents and information on radioactive waste in the scope of the Institute of Energy and Nuclear Research (IPEN) and with national regulation in Brazil. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗