SEARCH · Engineering Papers

Results for “Intelligence community”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 109 records · Page 6

All sources NASA NTRS DOE OSTI

DOE OSTI2025

Editorial: Visualizing offline and live data with AI (VOLDA) workshop first edition Princeton 11-13th June 2024

The first edition of the Visualizing Offline and Live Data with AI’ (VOLDA) Workshop took place at the Princeton University Campus, Mader Hall from 11 to 13 June 2024. This annual workshop held for the first time aims at bringing together the fusion community to discuss the challenges brought by Artificial Intelligence (AI) and visualizing large datasets in fusion experiment and simulation.

artificial intelligence↗

DOE OSTI2024

MSD CoP Webinar: AI and Extreme Events - Overcoming Data Challenges for Improved Characterization of Climate Extremes

Context: This webinar was hosted by the MultiSector Dynamics Community of Practice (MSD CoP; https://multisectordynamics.org). Abstract: Artificial Intelligence (AI) models require large volumes of data for training and testing. Data requirements present challenges for using AI to explore extreme events with limited observational data. This webinar will showcase two innovative methods developed by part of the European Climate Intelligence (CLINT) project to overcome data challenges and harness AI to improve our understanding of climate extremes. Dr. Ascenso will present his research on data augmentation methods to improve estimates of tropical cyclones using satellite data. His presentation will review established methods for data augmentation and explore opportunities and challenges for using generative AI to generate images of extreme, life-threatening tropical cyclones. Next, Dr. Plesiat will present his research on deep learning techniques to overcome limited observational data sets. His presentation will illustrate deep learning methods to develop AI reconstructions of four climate indices across Europe. Presenters : Dr. Guido Ascenso (post-doctoral researcher, Politecnico di Milano); Dr. Étienne Plésiat (German Climate Computing Centre - DKRZ) Moderator(s): Stefano Galelli (MSD CoP WG Co-Lead), David Gold (MSD CoP WG Co-Lead), Jillian Sturtevant (MSD CoP WG Communications Officer), Matteo Giuliani (Politecnico di Milano, MSD CoP WG Member, Moderator and Organizer) This webinar was held on: October 11, 2024 from 11AM - 1PM ET

AI↗

NASA NTRS1991

NASA gateway requirements analysis

NASA devotes approximately 40 percent of its budget to R&D. Twelve NASA Research Centers and their contractors conduct this R&D, which ranges across many disciplines and is fueled by information about previous endeavors. Locating the right information is crucial. While NASA researchers use peer contacts as their primary source of scientific and technical information (STI), on-line bibliographic data bases - both Government-owned and commercial - are also frequently consulted. Once identified, the STI must be delivered in a usable format. This report assesses the appropriateness of developing an intelligent gateway interface for the NASA R&D community as a means of obtaining improved access to relevant STI resources outside of NASA's Remote Console (RECON) on-line bibliographic database. A study was conducted to determine (1) the information requirements of the R&D community, (2) the information sources to meet those requirements, and (3) ways of facilitating access to those information sources. Findings indicate that NASA researchers need more comprehensive STI coverage of disciplines not now represented in the RECON database. This augmented subject coverage should preferably be provided by both domestic and foreign STI sources. It was also found that NASA researchers frequently request rapid delivery of STI, in its original format. Finally, it was found that researchers need a better system for alerting them to recent developments in their areas of interest. A gateway that provides access to domestic and international information sources can also solve several shortcomings in the present STI delivery system. NASA should further test the practicality of a gateway as a mechanism for improved STI access.

Duncan, Denise R.↗

DOE OSTI2013

Public Resistance is Waste-Based and What to Do About That - 13412

'Nuclear Communicators' connect a highly advanced sci-tech world with the world of everyday living. One challenge is helping stakeholders fit together three Big Ideas: (1) the valuable nuclear energy resource, (2) nuclear energy's invisible mortal dangers and potential bad-guy threats, and (3) critical scientific and engineering knowledge that is far over the heads of average (grade 8) USA reading levels. This article provides an overview of what does - and does not - work in our public communications. What does not work: 1. Going off topic. Address what concerns people most: how to manage nuclear wastes. 2. Underestimating public intelligence. What works: 1. Doing your homework on community history regarding nuclear materials. 2. Having discussion forums and public meetings with special guests from industry, government, and local leaders. 3. Regular cycles of communication with community groups to build a long-term dialogue. Solutions to some communication challenges require handling four gaps and one jungle: 1. Facts gap - Honest information about waste disposition. 2. Time gap - The timelines associated with waste management include 300 years, 10,000 years, and 703.8 million years. How do we talk about this? 3. Money gap - Who pays for new waste management challenges and technologies? 4. Confidence gap - Link local options to regional, national, and global knowledge. 5. Decision jungle - How can we make waste management rules and infrastructure more logical and transparent? Public communication needs to be grounded in facts for people who want to be credible actors in the new nuclear world. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

NASA NTRS2005

Large Scale Data Mining to Improve Usability of Data: An Intelligent Archive Testbed

Research in certain scientific disciplines - including Earth science, particle physics, and astrophysics - continually faces the challenge that the volume of data needed to perform valid scientific research can at times overwhelm even a sizable research community. The desire to improve utilization of this data gave rise to the Intelligent Archives project, which seeks to make data archives active participants in a knowledge building system capable of discovering events or patterns that represent new information or knowledge. Data mining can automatically discover patterns and events, but it is generally viewed as unsuited for large-scale use in disciplines like Earth science that routinely involve very high data volumes. Dozens of research projects have shown promising uses of data mining in Earth science, but all of these are based on experiments with data subsets of a few gigabytes or less, rather than the terabytes or petabytes typically encountered in operational systems. To bridge this gap, the Intelligent Archives project is establishing a testbed with the goal of demonstrating the use of data mining techniques in an operationally-relevant environment. This paper discusses the goals of the testbed and the design choices surrounding critical issues that arose during testbed implementation.

Ramapriyan, Hampapuram↗

DOE OSTI2021

Explainable Artificial Intelligence in Endocrinological Medical Research

Artificial Intelligence (AI) has been a part of the medical community for decades in the form of Clinical Decision Support Systems (CDSS) to aide physicians in diagnosis and categorization of patients (1). Recent years have seen a shift from expert-derived models to the integration of machine learning (ML) algorithms to drive the output of these AI systems due to the ability of ML models to more accurately make predictions by exploiting higher dimensional and often complex data. In many cases ML models gain their advantage in accuracy by capture complex and often non-linear relationships between features being used to make the prediction. However, the hype and excitement around these methods are tempered by the clinical utility of often black-box solutions driven by skepticism of results that are difficult for practitioners to not only interpret but explain to their patients (1-3). This skepticism is not unfounded as multiple examples of black-box solutions identifying incidental correlates as the key predictors have highlighted the potential bias in a training set, or reward system, that a ML model may exploit; for example a model discerning wolves from huskies based on snow in the background rather than features of the dogs (1, 4, 5).

Endocrinological, artificial intelligence, diabete↗

DOE OSTI2018

Health intelligence: how artificial intelligence transforms population and personalized health

Advances in computational and data sciences for data management, integration, mining, classification, filtering, visualization along with engineering innovations in medical devices have prompted demands for more comprehensive and coherent strategies to address the most fundamental questions in health care and medicine. Theory, methods, and models from artificial intelligence (AI) are changing the health care landscape in clinical and community settings and have already shown promising results in multiple applications in healthcare including, integrated health information systems, patient education, geocoding health data, social media analytics, epidemic and syndromic surveillance, predictive modeling and decision support, mobile health, and medical imaging (e.g. radiology and retinal image analyses). Health intelligence uses tools and methods from artificial intelligence and data science to provide better insights, reduce waste and wait time, and increase speed, service efficiencies, level of accuracy, and productivity in health care and medicine.

Shaban-Nejad, Arash↗

DOE OSTI2019

A Real-Time Big Data Control-Theoretical Framework for Cyber-Physical-Human Systems [Book Chapter]

It is known that cyber-physical-human systems naturally arise from interdependent infrastructure systems and smart connected communities. Such applications require ubiquitous information sensing and processing, intelligent machine-to-machine communication for a seamless coordination, as well as intelligent interactions between humans and machines. This chapter presents a control-theoretical framework to model heterogeneous physical dynamic systems, information and communication, as well as cooperative controls and/or distributed optimization of such interconnected systems. It is shown that efficient analytical and computational algorithms can be modularly designed and hierarchically implemented to operate and optimize cyber-physical-human systems, first to quantify individually the input–output relationship of nonlinear dynamic behaviors of every physical subsystem, then to coordinate locally both cyber-physical interactions of neighboring agents as well as physical-human interactions, and finally to dynamically model and optimize the overall networked system. The hierarchical structure makes the overall optimization and control problem scalable and solvable. Moreover, the three levels integrate individual designs and optimization, distributed cooperative optimization, and decision-making through real-time, data-driven, model-based learning and control. Specifically, one of the contributions of the chapter is to demonstrate how the combination of dissipativity theory and cooperative control serves as a natural framework and promising tools to analyze, optimize, and control such large-scale system. Application to digital grid is investigated as an illustrative example.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

SetBERT: the deep learning platform for contextualized embeddings and explainable predictions from high-throughput sequencing

MOTIVATION: High-throughput sequencing (HTS) is a modern sequencing technology used to profile microbiomes by sequencing thousands of short genomic fragments from the microorganisms within a given sample. This technology presents a unique opportunity for artificial intelligence to comprehend the underlying functional relationships of microbial communities. However, due to the unstructured nature of HTS data, nearly all computational models are limited to processing DNA sequences individually. This limitation causes them to miss out on key interactions between microorganisms, significantly hindering our understanding of how these interactions influence the microbial communities as a whole. Furthermore, most computational methods rely on post-processing of samples which could inadvertently introduce unintentional protocol-specific bias. RESULTS: Addressing these concerns, we present SetBERT, a robust pre-training methodology for creating generalized deep learning models for processing HTS data to produce contextualized embeddings and be fine-tuned for downstream tasks with explainable predictions. By leveraging sequence interactions, we show that SetBERT significantly outperforms other models in taxonomic classification with genus-level classification accuracy of 95%. Furthermore, we demonstrate that SetBERT is able to accurately explain its predictions autonomously by confirming the biological-relevance of taxa identified by the model. AVAILABILITY AND IMPLEMENTATION: All source code is available at https://github.com/DLii-Research/setbert. SetBERT may be used through the q2-deepdna QIIME 2 plugin whose source code is available at https://github.com/DLii-Research/q2-deepdna.

Ludwig, David W↗

NASA NTRS1988

Ada in AI or AI in Ada. On developing a rationale for integration

The use of Ada as an Artificial Intelligence (AI) language is gaining interest in the NASA Community, i.e., by parties who have a need to deploy Knowledge Based-Systems (KBS) compatible with the use of Ada as the software standard for the Space Station. A fair number of KBS and pseudo-KBS implementations in Ada exist today. Currently, no widely used guidelines exist to compare and evaluate these with one another. The lack of guidelines illustrates a fundamental problem inherent in trying to compare and evaluate implementations of any sort in languages that are procedural or imperative in style, such as Ada, with those in languages that are functional in style, such as Lisp. Discussed are the strengths and weakness of using Ada as an AI language and a preliminary analysis provided of factors needed for the development of criteria for the integration of these two families of languages and the environments in which they are implemented. The intent for developing such criteria is to have a logical rationale that may be used to guide the development of Ada tools and methodology to support KBS requirements, and to identify those AI technology components that may most readily and effectively be deployed in Ada.

Collard, Philippe E.↗

NASA NTRS1991

The NASA SETI program

In 1959, it was proposed that a sensible way to conduct interstellar communication would be to use radio at or near the frequency of hydrogen. In 1960, the first Search for Extraterrestrial Intelligence (SETI) was conducted using a radiotelescope at Green Bank in West Virginia. Since 1970, NASA has systematically developed a definitive program to conduct a sophisticated search for evidence of extraterrestrial intelligent life. The basic hypothesis is that life may be widespread in the univers, and that in many instances extraterrestrial life may have evolved into technological civilizations. The underlying scientific arguments are based on the continuously improving knowledge of astronomy and astrophysics, especially star system formation, and of planetary science, chemical evolution, and biological evolution. If only one in a million sun-like stars in our galaxy harbors species with cognitive intelligence, then there are 100,000 civilizations in the Milky Way alone. The fields of radioastronomy digital electronic engineering, spectrum analysis, and signal detection have advanced rapidly in the last twenty years and now allow for sophisticated systems to be built in order to attempt the detection of extraterrestrial intelligence signals. In concert with the scientific and engineering communities, NASA has developed, over the last several years, a Microwave Observing Project whose goal is to design, build, and operate SETI systems during the decade of the nineties in pursuit of the goal signal detection. The Microwave Observing Project is now approved and underway. There are two major components in the project: the Target Search Element and the Sky Survey Element.

Billingham, J.↗

NASA NTRS1996

CI Controls for Energy and Environment

Computational intelligence (CI) is a rapidly evolving field that utilizes life imitating metaphors for guiding model building including, but not limited to neural networks, fuzzy logic, genetic algorithms, artificial life, and hybrid CI paradigms. Although the boundaries between artificial intelligence (AI) and CI are not distinct, their research communities are separate and distinct. CI researchers tend to focus on processing numerical data from sensors, while the AI community generally relies on symbolic computing to capture human knowledge. In both areas, there is a great deal of interest and activity in hybrid systems that can offset the limitations of individual methods, extend their capabilities, and create new capabilities. Examples of the benefits that can accrue from hybrid systems are contained.

Biondo, Samuel J.↗

NASA NTRSDate not supplied

Beyond Fair: Engagement, Data Usability, and Open Community Productivity through the NASA Open Science Data Repository

The FAIR principle (findable, accessible, interoperable, and reusable) governs the storage and sharing of NASA space biology and health data[1]. These guiding principles maximize reuse of data and the reproducibility of scientific findings. The NASA Open Science Data Repository (OSDR; an expansion of NASA GeneLab) was built on the FAIR principles and houses over 500 studies and close to 1000 datasets from decades of space life sciences experiments. OSDR embodies the FAIR principles through data governance that includes mediated, embargoed, and fully open access data. The FAIR data governance principles were recently proposed to be expanded to encompass a FAIREST framework for assessing research data repositories (FAIR + Engagement, Social connections, and Trust)[2]. FAIREST emphasizes the importance of data repositories engaging with the scientific community and gaining the trust of researchers regarding data quality. Trust also refers to the TRUST principles developed for assessment of digital repositories: Transparency, Responsibility, User Focus, Sustainability, Technology[3]. We present the “Open Science for Life in Space” Analysis Working Groups (AWGs) as evidence regarding the power of engagement, social connections, and trust which has enhanced OSDR’s capabilities and productivity. AWG members engage in two main activities. One, members provide feedback on OSDR scientific standards for data ingestion, curation, and reuse (study, subject and assay metadata; processing pipelines; dataset formats and uniformed structures for machine-readability). Two, AWG members collaborate to mine-reuse OSDR data to conduct scientific analysis. With nearly 800 active members, the AWGs have resulted in 32 publications re-using OSDR data and contributed many papers in two major special issues in Cell (2020) and Nature (2024). AWGs also serve as networking groups, facilitate social connections between researchers at all levels of experience, and also have a social online ‘Forum’ used to keep members informed on projects and opportunities. This community-centric, productive, and trustworthy data culture has resulted in a broader effect with international space agencies, academics, and the commercial space sector wanting to submit their data to OSDR. Ten studies of Inspiration 4 data were recently publicly released by OSDR, as were some JAXA human data. Coming up soon in OSDR are data submissions from the European Space Agency, Virgin Galactic PIs, and SpaceX Polaris Dawn. A major benefit of OSDR is the array of standardized and uniformly formatted data (which was developed through AWG member consensus), from which visualization tools, analysis tools, and machine learning models can be built or trained. This talk will cover the Multi-Study Visualization Tool, the Environmental Data Application, RadLab, and a UCSF-NSF funded knowledge graph biomedical health discovery tool ‘SPOKE’ currently being integrated with OSDR. OSDR also provides training programs in bioinformatics and machine learning to improve the scientific community’s awareness of data availability and to boost their ability to perform data analysis. The increasing engagement of the scientific community and the public with technologies powered by artificial intelligence (AI) heightens the need for data analysis to be transparent. The AI for Life in Space initiative leverages the data products provided in OSDR to train AI models, with an emphasis on explainable and trustworthy AI, which would not be possible without FAIR data and metadata. Overall, here we will demonstrate the importance for NASA life sciences data repositories to adhere to the FAIREST framework, by providing examples and success stories from different aspects of OSDR.

data↗

DOE OSTI2019

Opportunities and Challenges for Human-Machine Teaming in Cybersecurity Operations [Panel Discussion]

Many of the challenges associated with cybersecurity operations are also ripe opportunities for the application of human-machine teaming. Advances in cognitive science, artificial intelligence, and machine learning promise the technology to achieve the level of intelligence and sophistication necessary to create a true intelligent teammate. This panel gathers experts from across the community to discuss the challenges and opportunities for human-machine teaming in cybersecurity operations.

Cybersecurity, Human-Machine Teaming↗

NASA NTRS1991

An architectural approach to create self organizing control systems for practical autonomous robots

For practical industrial applications, the development of trainable robots is an important and immediate objective. Therefore, the developing of flexible intelligence directly applicable to training is emphasized. It is generally agreed upon by the AI community that the fusion of expert systems, neural networks, and conventionally programmed modules (e.g., a trajectory generator) is promising in the quest for autonomous robotic intelligence. Autonomous robot development is hindered by integration and architectural problems. Some obstacles towards the construction of more general robot control systems are as follows: (1) Growth problem; (2) Software generation; (3) Interaction with environment; (4) Reliability; and (5) Resource limitation. Neural networks can be successfully applied to some of these problems. However, current implementations of neural networks are hampered by the resource limitation problem and must be trained extensively to produce computationally accurate output. A generalization of conventional neural nets is proposed, and an architecture is offered in an attempt to address the above problems.

Greiner, Helen↗

NASA NTRS2015

Improving Climate Projections Using "Intelligent" Ensembles

Recent changes in the climate system have led to growing concern, especially in communities which are highly vulnerable to resource shortages and weather extremes. There is an urgent need for better climate information to develop solutions and strategies for adapting to a changing climate. Climate models provide excellent tools for studying the current state of climate and making future projections. However, these models are subject to biases created by structural uncertainties. Performance metrics-or the systematic determination of model biases-succinctly quantify aspects of climate model behavior. Efforts to standardize climate model experiments and collect simulation data-such as the Coupled Model Intercomparison Project (CMIP)-provide the means to directly compare and assess model performance. Performance metrics have been used to show that some models reproduce present-day climate better than others. Simulation data from multiple models are often used to add value to projections by creating a consensus projection from the model ensemble, in which each model is given an equal weight. It has been shown that the ensemble mean generally outperforms any single model. It is possible to use unequal weights to produce ensemble means, in which models are weighted based on performance (called "intelligent" ensembles). Can performance metrics be used to improve climate projections? Previous work introduced a framework for comparing the utility of model performance metrics, showing that the best metrics are related to the variance of top-of-atmosphere outgoing longwave radiation. These metrics improve present-day climate simulations of Earth's energy budget using the "intelligent" ensemble method. The current project identifies several approaches for testing whether performance metrics can be applied to future simulations to create "intelligent" ensemble-mean climate projections. It is shown that certain performance metrics test key climate processes in the models, and that these metrics can be used to evaluate model quality in both current and future climate states. This information will be used to produce new consensus projections and provide communities with improved climate projections for urgent decision-making.

Baker, Noel C.↗

NASA NTRS2015

Improving Climate Projections Using "Intelligent" Ensembles

Baker, Noel C.↗

DOE OSTI2024

Perspectives on AI Architectures and Codesign for Earth System Predictability

Abstract Recently, the U.S. Department of Energy (DOE), Office of Science, Biological and Environmental Research (BER), and Advanced Scientific Computing Research (ASCR) programs organized and held the Artificial Intelligence for Earth System Predictability (AI4ESP) workshop series. From this workshop, a critical conclusion that the DOE BER and ASCR community came to is the requirement to develop a new paradigm for Earth system predictability focused on enabling artificial intelligence (AI) across the field, laboratory, modeling, and analysis activities, called model experimentation (ModEx). BER’s ModEx is an iterative approach that enables process models to generate hypotheses. The developed hypotheses inform field and laboratory efforts to collect measurement and observation data, which are subsequently used to parameterize, drive, and test model (e.g., process based) predictions. A total of 17 technical sessions were held in this AI4ESP workshop series. This paper discusses the topic of the AI Architectures and Codesign session and associated outcomes. The AI Architectures and Codesign session included two invited talks, two plenary discussion panels, and three breakout rooms that covered specific topics, including 1) DOE high-performance computing (HPC) systems, 2) cloud HPC systems, and 3) edge computing and Internet of Things (IoT). We also provide forward-looking ideas and perspectives on potential research in this codesign area that can be achieved by synergies with the other 16 session topics. These ideas include topics such as 1) reimagining codesign, 2) data acquisition to distribution, 3) heterogeneous HPC solutions for integration of AI/ML and other data analytics like uncertainty quantification with Earth system modeling and simulation, and 4) AI-enabled sensor integration into Earth system measurements and observations. Such perspectives are a distinguishing aspect of this paper. Significance Statement This study aims to provide perspectives on AI architectures and codesign approaches for Earth system predictability. Such visionary perspectives are essential because AI-enabled model-data integration has shown promise in improving predictions associated with climate change, perturbations, and extreme events. Our forward-looking ideas guide what is next in codesign to enhance Earth system models, observations, and theory using state-of-the-art and futuristic computational infrastructure.

54 ENVIRONMENTAL SCIENCES↗