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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.

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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
NASA NTRS2014

Vision Based Autonomous Robotic Control for Advanced Inspection and Repair

The advanced inspection system is an autonomous control and analysis system that improves the inspection and remediation operations for ground and surface systems. It uses optical imaging technology with intelligent computer vision algorithms to analyze physical features of the real-world environment to make decisions and learn from experience. The advanced inspection system plans to control a robotic manipulator arm, an unmanned ground vehicle and cameras remotely, automatically and autonomously. There are many computer vision, image processing and machine learning techniques available as open source for using vision as a sensory feedback in decision-making and autonomous robotic movement. My responsibilities for the advanced inspection system are to create a software architecture that integrates and provides a framework for all the different subsystem components; identify open-source algorithms and techniques; and integrate robot hardware.

sensory feedback↗
NASA NTRS2018

Progress Towards the CFD Vision 2030

Outline: NASA Motivation; CFD Vision 2030 Study Summary - The Vision - Roadmap - Validation Experiments - CFD 2030 Activities Since Report Release; T3 Technical Challenges Supporting CFD Vision 2030 - Revolutionary Computational Aerosciences (RCA) [Aerodynamics - Aerothermodynamics - Aerostructures - Aeroacoustics - Propulsion Integration] - Multidisciplinary Design, Analysis, and Optimization (MDAO) - Combustion Modeling - Innovative Measurements (Proposed); Implementation Plan, Going Forward; Summary.

Vision 2030↗
NASA NTRS2024

From Blood Platelets Classification to Earth System Digital Twins: A Career in Computer Vision

IEEE SIgnal Processing Society (SPS) and Women in Signal Processing (WiSP) Webinar (https://signalprocessingsociety.org/blog/webinar-wisp-blood-platelets-classification-earth-system-digital-twins-career-computer-vision): Join us for an interactive session with Dr. Jacqueline Le Moigne, Manager of the NASA Science Mission Directorate Earth Science Technology Office’s Advanced Information Systems Technology Program. In this webinar, Dr. Le Moigne will share her journey through academia, the private sector, and her pivotal roles at NASA, emphasizing her work in signal processing, computer vision, and related technologies. The webinar will conclude with an interactive Q&A session, providing attendees the opportunity to engage directly with Dr. Le Moigne about her experiences and insights. Dr. Jacqueline Le Moigne manages NASA's Advanced Information Systems Technology Program, focusing on novel technologies that support Earth Science missions. She previously held roles at NASA Goddard, including Assistant Chief for Technology, and has worked on NASA's Space Technology Research Grants Program and Technology Roadmaps. Before NASA, she was a Research Scientist at the University of Maryland's Computer Vision Laboratory and also gained experience in the private sector. Jacqueline earned her Ph.D. in Computer Science from the University Pierre and Marie Curie in Paris. Her research interests include Image Registration, Computer Vision, Artificial Intelligence, Autonomous Systems, Distributed Spacecraft Missions (DSM), and Earth System Digital Twins (ESDT), as well as high-performance and onboard computing. She has authored over 180 publications, including books and patents, and has received multiple awards, including the NASA Exceptional Service Medal and the Goddard Information Science and Technology Award in 2012.

Computer Vision; Image Processing; Earth Science R↗
NASA NTRSDate not supplied

Improving Sim-to-Real Transfer in Vision-Based Robot Navigation Via Instance-Level GAN-Based Data Augmentation

Achieving robust vision-based robotic tasks requires large amounts of data, which are often difficult to obtain in real-world scenarios. Simulators and synthetic data offer a cost-effective alternative, but the visual gap between simulation and reality hinders the performance of models when deployed in real-world environments. In this paper, we present a data augmentation pipeline that integrates a foundation model (Segment Anything Model) with an unsupervised image-to-image translation model (CycleGAN) for instance-level domain transfer from simulation to reality. This pipeline enables the generation of realistic labeled data from synthetic images for training supervised machine learning models in vision-based navigation tasks. We evaluate our approach on real-world data for ego-vehicle pose estimation, a critical autonomous navigation task involving the prediction of cross-track position and heading angle relative to road center line markings. The results of our tests show that our GAN-based data augmentation pipeline significantly outperforms models trained solely on simulation data or on data processed with standard image augmentation methods for sim-to-real transfer, enhancing model robustness and generalizability in real-world scenarios. Our method provides a scalable and flexible data augmentation tool for leveraging large synthetic datasets to enhance vision-based robotic navigation tasks.

artificial intelligence↗
DOE OSTI2015

Wind Vision: A New Era for Wind Power in the United States (Highlights); U.S. Department of Energy (DOE), NREL (National Renewable Energy Laboratory)

This is a four-part Wind Vision project, consisting of Wind Vision Highlights, Executive Summary, a Full Report, and Appendix. The U.S. Department of Energy (DOE) Wind Program, in close cooperation with the wind industry, led a comprehensive analysis to evaluate future pathways for the wind industry. The Wind Vision report updates and expands upon the DOE's 2008 report, 20% Wind Energy by 2030, and defines the societal, environmental, and economic benefits of wind power in a scenario with wind energy supplying 10% of national end-use electricity demand by 2020, 20% by 2030, and 35% by 2050.

17 WIND ENERGY↗
DOE OSTI2021

A METHODOLOGY TO SUPPORT THE DEVELOPMENT OF A NEW STATE VISION FOR THE UNITED STATES NUCLEAR INDUSTRY

A new strategy in the way in which the United States nuclear power plants (NPPs) are operated, maintained, and supported is needed. One such strategy is to transform the NPP operating model through a business-driven approach that leverages technology to enable new capabilities that improve performance and reduce cost. This paper presents a methodology for developing an achievable yet transformative new state vision that ensures continued safe and efficient operations of the United States NPP fleet. This work builds on existing guidance and leverages previous research to comprehensively address both bottom-up (i.e., utility needs) and top-down (i.e., first principles) considerations important for developing a new state vision. The proposed methodology is intended to provide industry-wide guidance for developing a new state vision that leverages both the selected vendor’s capabilities in a way that meets the utility’s modernization goals while ensuring state-of-the-art systems engineering and human factors engineering principles are applied that promote overall plant safety, performance, and efficiency.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2019

Restoration of high-sensitivity and adapting vision with a cone opsin

Inherited and age-related retinal degenerative diseases cause progressive loss of rod and cone photoreceptors, leading to blindness, but spare downstream retinal neurons, which can be targeted for optogenetic therapy. However, optogenetic approaches have been limited by either low light sensitivity or slow kinetics, and lack adaptation to changes in ambient light, and not been shown to restore object vision. We find that the vertebrate medium wavelength cone opsin (MW-opsin) overcomes these limitations and supports vision in dim light. MW-opsin enables an otherwise blind retinitis pigmenotosa mouse to discriminate temporal and spatial light patterns displayed on a standard LCD computer tablet, displays adaption to changes in ambient light, and restores open-field novel object exploration under incidental room light. By contrast, rhodopsin, which is similar in sensitivity but slower in light response and has greater rundown, fails these tests. Thus, MW-opsin provides the speed, sensitivity and adaptation needed to restore patterned vision.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Ultrafast structural changes direct the first molecular events of vision

Vision is initiated by the rhodopsin family of light-sensitive G protein-coupled receptors (GPCRs). A photon is absorbed by the 11-cis retinal chromophore of rhodopsin, which isomerizes within 200 femtoseconds to the all-transconformation, thereby initiating the cellular signal transduction processes that ultimately lead to vision. However, the intramolecular mechanism by which the photoactivated retinal induces the activation events inside rhodopsin remains experimentally unclear. Here we use ultrafast time-resolved crystallography at room temperature to determine how an isomerized twisted all-trans retinal stores the photon energy that is required to initiate the protein conformational changes associated with the formation of the G protein-binding signalling state. The distorted retinal at a 1-ps time delay after photoactivation has pulled away from half of its numerous interactions with its binding pocket, and the excess of the photon energy is released through an anisotropic protein breathing motion in the direction of the extracellular space. Notably, the very early structural motions in the protein side chains of rhodopsin appear in regions that are involved in later stages of the conserved class A GPCR activation mechanism. Our study sheds light on the earliest stages of vision in vertebrates and points to fundamental aspects of the molecular mechanisms of agonist-mediated GPCR activation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Accuracy improvement in a calibration test bench for accelerometers by a vision system

A procedure is described in this paper for the accuracy improvement of calibration of low-cost accelerometers in a prototype rotary test bench, driven by a brushless servo-motor and operating in a low frequency range of vibrations (0 to 5 Hz). Vibration measurements by a vision system based on a low frequency camera have been carried out, in order to reduce the uncertainty of the real acceleration evaluation at the installation point of the sensor to be calibrated. A preliminary test device has been realized and operated in order to evaluate the metrological performances of the vision system, showing a satisfactory behavior if the uncertainty measurement is taken into account. A combination of suitable settings of the control parameters of the motion control system and of the information gained by the vision system allowed to fit the information about the reference acceleration at the installation point to the needs of the procedure for static and dynamic calibration of three-axis accelerometers.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Pre-training Vision Models for the Classification of Alerts from Wide-field Time-domain Surveys

Modern wide-field time-domain surveys facilitate the study of transient, variable and moving phenomena by conducting image differencing and relaying alerts to their communities. Machine learning tools have been used on data from these surveys and their precursors for more than a decade, and convolutional neural networks (CNNs), which make predictions directly from input images, saw particularly broad adoption through the 2010s. Since then, continually rapid advances in computer vision have transformed the standard practices around using such models. It is now commonplace to use standardized architectures pre-trained on large corpora of everyday images (e.g., ImageNet). In contrast, time-domain astronomy studies still typically design custom CNN architectures and train them from scratch. Here, we explore the effects of adopting various pre-training regimens and standardized model architectures on the performance of alert classification. We find that the resulting models match or outperform a custom, specialized CNN like what is typically used for filtering alerts. Moreover, our results show that pre-training on galaxy images from Galaxy Zoo tends to yield better performance than pre-training on ImageNet or training from scratch. We observe that the design of standardized architectures are much better optimized than the custom CNN baseline, requiring significantly less time and memory for inference despite having more trainable parameters. On the eve of the Legacy Survey of Space and Time and other image-differencing surveys, these findings advocate for a paradigm shift in the creation of vision models for alerts, demonstrating that greater performance and efficiency, in time and in data, can be achieved by adopting the latest practices from the computer vision field.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

DeepAndes: A Self-Supervised Vision Foundation Model for Multispectral Remote Sensing Imagery of the Andes

By mapping sites at large scales usingremotely sensed data, archaeologists can generate unique insights into long-term demographic trends, interregional social networks, and human adaptations in the past. Remote sensing surveys complement field-based approaches, and their reach can be especially great when combined with deep learning and computer vision techniques. However, conventional supervised deep learning methods face challenges in annotating fine-grained archaeological features at scale. In addition, while recent vision foundation models have shown remarkable success in learning large-scale remote sensing data with minimal annotations, most off-the-shelf solutions are designed for RGB images rather than multispectral satellite imagery, such as the eight-band data used in our study. In this article, we introduce DeepAndes, a transformer-based vision foundation model trained on three million multispectral satellite images, specifically tailored for Andean archaeology. DeepAndes incorporates a customized DINOv2 self-supervised learning algorithm optimized for eight-band multispectral imagery, marking the first foundation model designed explicitly for the Andes region. We evaluate its image understanding performance through imbalanced image classification, image instance retrieval, and pixel-level semantic segmentation tasks. Our experiments show that DeepAndes achieves superior F1 scores, mean average precision, and Dice scores in few-shot learning scenarios, significantly outperforming models trained from scratch or pretrained on smaller datasets. This underscores the effectiveness of large-scale self-supervised pretraining in archaeological remote sensing.

Guo, Junlin [Vanderbilt Univ., Nashville, TN (Unit↗
DOE OSTI2016

Identifying local structural states in atomic imaging by computer vision

Abstract The availability of atomically resolved imaging modalities enables an unprecedented view into the local structural states of materials, which manifest themselves by deviations from the fundamental assumptions of periodicity and symmetry. Consequently, approaches that aim to extract these local structural states from atomic imaging data with minimal assumptions regarding the average crystallographic configuration of a material are indispensable to advances in structural and chemical investigations of materials. Here, we present an approach to identify and classify local structural states that is rooted in computer vision. This approach introduces a definition of a structural state that is composed of both local and nonlocal information extracted from atomically resolved images, and is wholly untethered from the familiar concepts of symmetry and periodicity. Instead, this approach relies on computer vision techniques such as feature detection, and concepts such as scale invariance. We present the fundamental aspects of local structural state extraction and classification by application to simulated scanning transmission electron microscopy images, and analyze the robustness of this approach in the presence of common instrumental factors such as noise, limited spatial resolution, and weak contrast. Finally, we apply this computer vision-based approach for the unsupervised detection and classification of local structural states in an experimental electron micrograph of a complex oxides interface, and a scanning tunneling micrograph of a defect-engineered multilayer graphene surface.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2018

Interoperability Strategic Vision

The Interoperability Strategic Vision whitepaper aims to promote a common understanding of the meaning and characteristics of interoperability and to provide a strategy to advance the state of interoperability as applied to integration challenges facing grid modernization. This includes addressing the quality of integrating devices and systems and the discipline to improve the process of successfully integrating these components as business models and information technology improve over time. The strategic vision for interoperability described in this document applies throughout the electric energy generation, delivery, and end-use supply chain. Its scope includes interactive technologies and business processes from bulk energy levels to lower voltage level equipment and the millions of appliances that are becoming equipped with processing power and communication interfaces. A transformational aspect of a vision for interoperability in the future electric system is the coordinated operation of intelligent devices and systems at the edges of grid infrastructure. This challenge offers an example for addressing interoperability concerns throughout the electric system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Developing a Human Factors Engineering Program Plan and End State Vision to Support Full Nuclear Power Plant Modernization

The Light Water Reactor Sustainability (LWRS) Program, which is sponsored by the United States (U.S.) Department of Energy (DOE), conducts research and development (R&D) to establish the technical bases to sustain U.S. nuclear assets. One area in the LWRS program is the Plant Modernization pathway, which includes human factors R&D, human factors engineering (HFE), and ergonomics. LWRS program researchers in this pathway conduct targeted R&D to address aging and reliability concerns with the legacy instrumentation and control (I&C) and related information systems in commercial nuclear power plants (NPPs). One key aspect of this pathway is to develop a strategy for full nuclear plant modernization, which previous LWRS plant modernization milestone reports have begun to describe. To further support this comprehensive plan and vision for full plant modernization, additional details need to be further developed. In particular, a core aspect of a strategy for full nuclear plant modernization is an HFE program plan and an end state vision for plant modernization that is well-integrated with both 1) a technically defensible approach to migrating the existing, mostly analog I&C infrastructure to a digital I&C infrastructure, and 2) a valid business case methodology to cost-justify the modernization activity. Thus, the purpose of this LWRS R&D activity and milestone report is threefold. First, the report provides guidance for operating commercial NPPs to support their development and evaluation of an HFE program management plan (HFE PMP) and an end state vision for plant modernization. The report presents best practices and lessons learned related to HFE PMPs based on MCR modifications in which analog systems were replaced by digital I&C systems. Second, the report summarizes the rationale for full digital I&C integration, highlights a number of factors that are critical to consider, and describes how HFE should merge with full digital I&C integration. Finally, the report summarizes and integrates the economic factors that must be considered to cost-justify HFE involvement in plant modernization.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2016

Hydropower Vision A New Chapter for America's 1st Renewable Electricity Source

The U.S. Department of Energy’s (DOE’s) Wind and Water Power Technologies Office has led a first-of-its- kind comprehensive analysis to evaluate future pathways for low-carbon, renewable hydropower (hydropower generation and pumped storage) in the United States, focused on continued technical evolution, increased energy market value, and environmental sustainability. Undertaken through a broad-based collaborative effort, the Hydropower Vision initiative had four principal objectives: characterize the current state of hydropower in the United States, including trends, opportunities, and challenges; identify ways for hydropower to maintain and expand its contributions to the electricity and water management needs of the nation from the present through 2030 and 2050; examine critical environmental and social factors to assess how existing hydropower operations and potential new projects can minimize adverse effects, reduce carbon emissions from electricity generation, and contribute to stewardship of waterways and watersheds; and develop a roadmap identifying stakeholder actions that could support responsible ongoing operations and potential expansion of hydropower facilities. The Hydropower Vision analysis finds that U.S. hydropower could grow from 101 gigawatts (GW) of capacity to nearly 150 GW by 2050. Growth under this modeled scenario would result from a combination of 13 GW of new hydropower generation capacity (upgrades to existing plants, adding power at existing dams and canals, and limited development of new stream-reaches), and 36 GW of new pumped storage capacity. If this level of growth is achieved, benefits such as a savings of $209 billion from avoided greenhouse gas (GHG) emissions could be realized, of which $185 billion would be attributable to operation of the existing hydropower fleet. Transformative technical innovations able to meet the co-objectives of environmental sustainability and low-carbon energy will be critical to enabling additional hydropower growth beyond these levels. The Hydropower Vision report specifically does not evaluate or recommend new policy actions but instead analyzes the feasibility and certain benefits and costs of various credible scenarios, all of which could inform policy decisions at the federal, state, tribal, and local levels.

13 HYDRO ENERGY↗
DOE OSTI2019

Goals, Policies and Actions that will Affect Improved Outcomes in District Heating in Ukraine: Summary of Policy Vision for Transformation

This policy paper lays out a vision for reforming the Ukrainian district heating system. This includes goals, policy priorities and actions to achieve that vision. This vision describes the transformation of the sector in the long-term, with sequencing to achieve real results starting in the near term.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Vision Transformers Explained Series

Since their introduction in 2017 with Attention is All You Need¹, transformers have established themselves as the state of the art for natural language processing (NLP). In 2021, An Image is Worth 16x16 Words successfully adapted transformers for computer vision tasks. Since then, numerous transformer-based architectures have been proposed for computer vision. This article walks through the Vision Transformer (ViT) as laid out in An Image is Worth 16x16 Words.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

A Smart Vision-Aided RICH (Robotic Interface Control and Handling) System for VULCAN

High-flux neutron beams and high-efficiency detectors enable rapid neutron diffraction measurements at the Engineering Materials Diffractometer (VULCAN) at the Spallation Neutron Source (SNS), Oak Ridge National Laboratory (ORNL). To optimize beam time utilization, efficient sample exchange, alignment, and automated measurements are essential. Recent advances in artificial intelligence (AI) have expanded the capabilities of robotic systems. Here, we report the development of a Robotic Interactive Control and Handling (RICH) system for sample handling at VULCAN, designed to support high-throughput experiments and reduce overhead time. The RICH system employs a six-axis desktop robot integrated with AI-based computer vision models capable of recognizing and localizing samples in real time from instrument and depth-resolving cameras. Vision algorithms combine these detections to align samples with designated measurement positions or place them within complex sample environments such as furnaces. This integration of machine learning-assisted vision with robotic handling demonstrates the feasibility of autonomous sample detection and preparation, offering a pathway toward fully unmanned neutron scattering experiments.

automation↗