Results for “computer vision” — Page 77

NASA NTRSDate not supplied

Clinical Decision Support - Concepts of Operation

We are entering a new era in space exploration to return to the moon and explore Mars. These ambitious goals will require significant changes to in-flight and habitat medical care due to constraints on mass, volume, power, crew time and medical evacuation capabilities. These constraints make it absolutely necessary to develop transformative solutions using new technologies. The Exploration Medical Capability (ExMC) Element of the Human Research Program (HRP) pushes the boundary of space medical systems to advance the care of astronauts on future exploration missions beyond low Earth orbit by identifying and testing next-generation medical care and crew health maintenance technologies. The Clinical Decision Support (CDS) project addresses the gap Medical-701 within the Inflight Medical Conditions risk: Enhance medical capabilities within an exploration medical system. For long-duration, deep space missions, computational and data resources will play an important role in maintaining crew health, wellness and performance where the crew will need to be more self-reliant. The aim of the CDS project is to develop and provide recommended requirements for an in-vehicle CDSS that acts as an assistant for delivering optimal health and performance and medical care during exploration missions. The CDSS is envisioned as a software-based tool that will augment a crewmembers’ knowledge, skills and abilities to assist in decision-making and crew health and performance (CHP) management thus increasing CHP systems capabilities. The human interface will be context aware and lessen the cognitive load to assimilate and use information as well as combine large disparate data sets in such a manner that provides the crew with actionable insight to decisions related to crew medical, health and performance management. Crew autonomy will be provided through a CDS that presents knowledge and data in a context aware manner to augment a crew members’ knowledge, skills and abilities during the process of observation, orientation, decisions and action. The CDS project addresses the need for crew members to operate independently during long duration space exploration missions that require medical Levels of Care (LoC) V, the highest level specified by NASA-STD-3001 and described in more detail by the ExMC interpretation of LoC document (NASA/TM-2017-219290), where significant changes to in-flight and habitat medical care necessitate increasing crew autonomy in decision making and task performance. The CDS project will develop and test a series of iterative and increasingly more complex system prototypes. These annual demonstrations of the data system integration with the crew health and performance domain will inform exploration medical system requirements for an on-board Clinical Decision Support System (CDSS) through a series of use cases that guide CDS prototype functionality. CDS concepts are based on ExMC Concept of Operations documents (presented separately) and will highlight architecture extensibility to other more complex analyses and tests using core crew health and performance integrated data management, processing and visualization capabilities. This approach also establishes how externally developed analyses and approaches could be added to expand a clinical decision support system and thus highlight how a comprehensive system can be commercially and/or globally developed. The CDS project will build upon the concept of an integrated data management approach based on the Medical Data Architecture (MDA) project to more fully address challenges associated with in-flight and habitat medical, health and performance care due to constraints on mass, volume, power, crew time and medical evacuation capabilities required for medical LoC V. These requirements will be derived through systems engineering approaches and software prototype developments over the course of the multi-year CDS project to address crew health and performance decision-making and task performance, often autonomously executed by the crew, in a manner that is consistent with the appropriate medical level of care for the mission. This presentation will provide an overview of the vision for the CDS project and highlight the initial accomplishments in project planning, implementation and requirements identification in fiscal year 2020.

clinical decision support↗

NASA NTRSDate not supplied

Clinical Decision Support - Overview and Update

We are entering a new era in space exploration to return to the moon and explore Mars. These ambitious goals will require significant changes to in-flight and habitat medical care due to constraints on mass, volume, power, crew time and medical evacuation capabilities. These constraints make it absolutely necessary to develop transformative solutions using new technologies. The Exploration Medical Capability (ExMC) Element of the Human Research Program (HRP) pushes the boundary of space medical systems to advance the care of astronauts on future exploration missions beyond low Earth orbit by identifying and testing next-generation medical care and crew health maintenance technologies. The Clinical Decision Support (CDS) project addresses the gap Medical-701 within the Inflight Medical Conditions risk: Enhance medical capabilities within an exploration medical system. For long-duration, deep space missions, computational and data resources will play an important role in maintaining crew health, wellness and performance where the crew will need to be more self-reliant. The aim of the CDS project is to develop and provide recommended requirements for an in-vehicle CDSS that acts as an assistant for delivering optimal health and performance and medical care during exploration missions. The CDSS is envisioned as a software-based tool that will augment a crewmembers’ knowledge, skills and abilities to assist in decision-making and crew health and performance (CHP) management thus increasing CHP systems capabilities. The human interface will be context aware and lessen the cognitive load to assimilate and use information as well as combine large disparate data sets in such a manner that provides the crew with actionable insight to decisions related to crew medical, health and performance management. Crew autonomy will be provided through a CDS that presents knowledge and data in a context aware manner to augment a crew members’ knowledge, skills and abilities during the process of observation, orientation, decisions and action. The CDS project addresses the need for crew members to operate independently during long duration space exploration missions that require medical Levels of Care (LoC) V, the highest level specified by NASA-STD-3001 and described in more detail by the ExMC interpretation of LoC document (NASA/TM-2017-219290), where significant changes to in-flight and habitat medical care necessitate increasing crew autonomy in decision making and task performance. The CDS project will develop and test a series of iterative and increasingly more complex system prototypes. These annual demonstrations of the data system integration with the crew health and performance domain will inform exploration medical system requirements for an on-board Clinical Decision Support System (CDSS) through a series of use cases that guide CDS prototype functionality. CDS concepts are based on ExMC Concept of Operations documents (presented separately) and will highlight architecture extensibility to other more complex analyses and tests using core crew health and performance integrated data management, processing and visualization capabilities. This approach also establishes how externally developed analyses and approaches could be added to expand a clinical decision support system and thus highlight how a comprehensive system can be commercially and/or globally developed. The CDS project will build upon the concept of an integrated data management approach based on the Medical Data Architecture (MDA) project to more fully address challenges associated with in-flight and habitat medical, health and performance care due to constraints on mass, volume, power, crew time and medical evacuation capabilities required for medical LoC V. These requirements will be derived through systems engineering approaches and software prototype developments over the course of the multi-year CDS project to address crew health and performance decision-making and task performance, often autonomously executed by the crew, in a manner that is consistent with the appropriate medical level of care for the mission. This presentation will provide an overview of the vision for the CDS project and highlight the initial accomplishments in project planning, implementation and requirements identification in fiscal year 2020.

clinical decision support system↗

DOE OSTI2026

Intelligent Experiments Through Real-time AI: Fast Data Processing and Autonomous Detector Control for sPHENIX and Future EIC Detectors (Final Report)

The overall vision of this project was to integrate real-time artificial intelligence (AI) directly into the data acquisition and detector-control systems of nuclear physics experiments, including both fast online event selection and an autonomous detector-control feedback loop. The work carried out under the award focused on the fast online event-selection half of that vision: the efficient recording of low-momentum heavy-flavor (HF) hadron decays in proton-proton collisions at the sPHENIX experiment at the Relativistic Heavy Ion Collider (RHIC)—an observable that requires fast tracking and topological trigger selection not previously demonstrated at RHIC, and that is essential for QCD studies at future facilities such as the Electron-Ion Collider (EIC). The autonomous detector-control (GPU-based feedback) component named in the project title remained a design concept and was not implemented under this award. The Massachusetts Institute of Technology (MIT) group led the offline simulation and data processing needed to train the machine-learning (ML) models, the translation of trained models to Field-Programmable Gate Array (FPGA) firmware using the hls4ml framework, and the physics validation of heavy-flavor reconstruction. Over the award period, the team developed and hardware-tested the principal components of an AI-based heavy-flavor trigger on simulated and recorded sPHENIX tracker data: a software Bipartite Graph Attention Network (BiGAT) trigger model reaching > 95% signal efficiency at 99% background rejection; an FPGA-native hit clusterizer matching the offline clustering; smaller networks synthesized to FPGA within the required sub-10 µs latency; and an assembled decoder–clusterizer–inference firmware chain exercised on the FELIX readout board. A complete, fully integrated hardware demonstrator was not finished within the award period. This report documents the project goals, the MIT group’s contributions, the technical accomplishments, and the outlook toward applications at the future EIC ePIC detector.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2020

Understanding Structures of Cyber Competition in an Era of Major Power Rivalry

Over the past two decades, the cyber domain has emerged and evolved into a key strategic domain for nations across the globe. Security strategies are espoused by heads of state that focus on how to manage the increasingly enormous, crosscutting impact that the cyber domain has on national security across economic, military, intelligence, intellectual property, and countless other facets. These strategies frequently evolve, and even change entirely, as leaders adapt to new technologies and administrations change. Even if these strategies did not change at all, they would take inordinate amounts of time to effectively implement within the organizational structures of government. The time required to go from setting department and agency-level goals, to the time small teams have well-oiled processes and expertise to accomplish tactical objectives that meet the strategic vision is lengthy. With near certainty, by the time objectives and vision are implemented, the landscape, strategy, or both has changed entirely. While this churn will likely never cease due to the rapidly changing nature of the cyber domain, this problem raises an important question: can governmental structures be organized to rapidly adapt to changing cyber strategies? As offices responsible for particular missions in cyberspace shuffle about within the bureaucracy, are technical capabilities enabled or enhanced? No matter how advanced a particular technical capability is or how adept the staff is at solving problems, they will be ineffective if placed haphazardly within the organization: the right authorities may not exist for their office, the correct lines of interpersonal communication may not be established properly, or insufficient resources have not been allocated to effectively deploy a brilliant technical solution. This concept of organizational agility in the context of national cyber capabilities is important when taking into account the National Defense Strategy’s emphasis on cyber capability and the ability of the United States to compete and rapidly adapt to new challenges posed by rivals. As a nation, we are at a point where technology evolves rapidly enough to warrant thoughtful and nimble changes to the bureaucratic structures that support how cyber operations are carried out. Taking these questions and cross-comparing them to the organizational structures across China, Russia, and the United States provides for an interesting thought experiment. As non-democratic regimes, China and Russia have differing priorities and internal power dynamics than the United States and thus organize their governments differently. By combining known and broadcasted strategies of these nations with the observed technical capabilities demonstrated in the public domain, we can begin to see how organizational structures map to strategic goals and directly enable technical capabilities. Insight can be gained by introducing organizational structures into traditional analysis focusing solely around strategies and capabilities; additionally, otherwise unknown capabilities or intents might be discovered or inferred by analyzing organizational structures alone. Analyzing cyber operations from this oft-overlooked perspective could potentially provide useful insight and more concrete actions that can be undertaken to realize the National Defense Strategy’s goals.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2020

North Dakota Integrated Carbon Storage Complex Feasibility Study. Final report

In spring 2017, the Energy & Environmental Research Center (EERC) initiated an effort to determine the feasibility of developing a commercial-scale CO 2 geologic storage complex able to store 50+ million tonnes (Mt) of CO 2 in central North Dakota safely, permanently, and economically. The objective was to fulfill the goals of the U.S. Department of Energy (DOE) Carbon Storage Assurance Facility Enterprise (CarbonSAFE) Initiative and address technical and nontechnical challenges specific to commercial-scale deployment of a CO2 storage project. The findings clearly show that the concept of capturing CO 2 from a lignite-fired electrical generation facility in central North Dakota and safely and permanently storing the CO 2 in the deep subsurface is indeed technically, economically, and socially feasible. This project evaluated two study areas and their respective geologic storage complexes located adjacent to separate coal-fired facilities in North Dakota: the Basin Electric Power Cooperative (BEPC)-owned Great Plains Synfuels Plant (GPSP) and the Minnkota Power Cooperative (Minnkota)-owned Milton R. Young Station (MRYS). These locations, one with CO 2 capture in place and an existing CO 2 pipeline, are bolstered by progressive North Dakota pore space ownership and long-term liability laws. These elements and a motivated team created an ideal synergistic scenario for ensuring success of the CarbonSAFE Initiative and promoting North Dakota’s statewide vision for carbon management. The project included drilling two new geologic characterization wells, integrating an existing 3-D seismic survey, creating a geologic model subsequently used for injection simulation, a risk assessment, public outreach, and generating a site development plan based on results. In addition, the performance of select National Risk Assessment Partnership tools was evaluated. The geologic characterization wells were drilled ~5600 feet deep to the Broom Creek Formation; ~350 feet of core was retrieved from each well. The core included the Broom Creek (targeted injection zone) and a portion of the overlying Opeche Shale (seal). The Flemmer-1 well, west of Beulah, North Dakota, yielded 169 feet of sandstone. The BNI-1 well located south of Center, North Dakota, yielded 124 feet of sandstone. In each case, laboratory analysis of the sandstone showed permeability in the 300–1000-mD range, with porosity of 20%–30%. The Flemmer-1 well was sited within the boundaries of an existing 3-D seismic survey. Colocating the well with the seismic survey maximized the relationship between new and legacy data and developed a first-of-its-kind interpretation of the geologic fabric of the Broom Creek. Geologic characterization data were integrated into a 5544-mi 2 geocellular model that encompassed both new wells and stratigraphy from the surface to the Amsden Formation (underlying the Broom Creek). The model was later expanded vertically to include the deeper Black Island–Deadwood interval to examine its potential viability as a storage target. The geocellular model provided the foundation for dynamic simulation of CO 2 into the Broom Creek. Results of the simulation suggest that the Broom Creek could accept the DOE target rate of 2 Mt/yr of CO 2 into as few as two wells. To bracket the expected capture from MRYS, simulations were also investigated for a 4-Mt/yr rate near MRYS. Although more wells are needed (two additional), the Broom Creek still has the storage resource to accept the CO 2 at the increased rate. A risk assessment exercise was conducted to identify and assess technical and nontechnical risks that could prevent potential candidate storage complexes within the study area from serving as commercial storage sites. The assessment identified and evaluated six technical risk categories: 1) CO 2 injectivity, 2) storage capacity, 3) lateral migration of CO 2 , 4) lateral pressure propagation, 5) vertical migration of CO 2 or formation brine, and 6) induced seismicity. Following two rounds of analysis and scoring, no risks were determined to preclude continued efforts to develop carbon capture, utilization, and storage (CCUS) in central North Dakota. The risk assessment results will be used to guide future site characterization, modeling and simulation, and monitoring activities. A specific nontechnical strategic risk based on challenges that may be realized in amalgamation of pore space resulted in vertically expanding the geologic model to incorporate the potential for stacked storage in multiple saline reservoirs. By including the Black Island–Deadwood interval (the basal sedimentary reservoir in this region), the amalgamated areal extent could be reduced by as much as 45%. Working with a smaller geographic area reduces risks and costs associated with monitoring and pore space leasing. An economic evaluation incorporating capture; transport (<5 mi); Class VI wells; permitting; and monitoring, verification, and accounting suggests implementing commercial-scale CCUS is economically attractive if the federal tax benefits of 45Q are included. This is validated by Minnkota’s continued pursuit of CO 2 capture and geologic storage at MRYS through its Project Tundra initiative, indicating that there is a business case for CCUS in central North Dakota. Currently, North Dakota is the only state with underground injection control (UIC) Class VI primacy. Built into the North Dakota Century Code is a series of regulatory requirements that guide the process to obtain a Class VI CO 2 storage facility permit. As part of this project, a site development plan was compiled to assure compliance with North Dakota’s requirements to permit a commercial-scale CO 2 storage operation and includes a prospective time line encompassing a general breakdown of activities. In total, an estimated 30 months would be needed to execute the necessary steps to attain a North Dakota CO 2 storage facility permit. Outreach was an integral part of the project and encompassed any project-related activity that had contact or exposure beyond the project team. The goals of outreach were to foster an environment from which stakeholders could make informed decisions about the project and gauge community receptiveness to a CCUS project. A consistent set of messages and outreach products were developed in conjunction with an outreach advisory board that integrated project partners and team members. To gauge public acceptability of geologically storing CO 2 , 5611 households in the project area were invited to participate in an online survey. The survey results indicate that the public attitude regarding CCUS is neutral to positive, with strong sentiment that CO 2 capture and storage may be an approach to maintain the economic vitality of the region. To achieve project objectives, critical support in the form of financial backing, engineering evaluations, site access, outreach collaboration, operations data, risk assessment/evaluation, and software access was provided by BEPC, the North Dakota Industrial Commission Lignite Research Council, ALLETE Clean Energy, BNI Energy, North American Coal Corporation, Minnkota, Prairie Public Broadcasting, Computer Modelling Group Ltd., and Schlumberger.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2026

Discovering Strong Gravitational Lenses in the Dark Energy Survey with Interactive Machine Learning and Crowd-sourced Inspection with Space Warps

We conduct a search for strong gravitational lenses in the Dark Energy Survey (DES) Year 6 imaging data. We implement a pre-trained Vision Transformer (ViT) for our machine learning (ML) architecture and adopt interactive machine learning to construct a training sample with multiple classes to address common types of false positives. Our ML model reduces ∼236 million DES cutout images to 22,564 targets of interest, including ∼85% of previously reported galaxy–galaxy lens candidates discovered in DES. These targets were visually inspected by citizen scientists, who ruled out ∼90% as false positives. Of the remaining 2618 candidates, 149 were expert-classified as “definite” lenses and 516 as “probable” lenses, for a total of 665 systems, with 147 of these candidates being newly identified. Additionally, we trained a second ViT to find double-source plane lens systems, finding at least one double-source system. Our main ViT excels at identifying galaxy–galaxy lenses, consistently assigning high scores to candidates with high expert assessments. The top 800 ViT-scored images include ∼100 of our “definite” lens candidates. This selection is an order of magnitude higher in purity than previous convolutional neural-network-based lens searches and demonstrates the feasibility of applying our methodology for discovering large samples of lenses in future surveys.

79 ASTRONOMY AND ASTROPHYSICS↗