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This dataset provides the following on road testing data: - Videos - In-vehicle dash camera videos during different testing scenarios. - Signal controller data - NTCIP log data and processed signal timing data from the corresponding signal controllers - Vehicle data - Vehicle data recorded during the testing, including GNSS, communication, CAN signals.
This project was part of the Characterizing Behaviors and Capabilities for Emerging Connected and Automated Vehicle Technologies, Sensors, and Connectivity project. The National Laboratory of the Rockies partnered with Cummins Inc. to collect data from Class 8 tractor trailer combinations in platoon (cooperative adaptive cruise control) operations on public roads in southern Indiana. Data collected include J1939 CAN bus, radar, intervehicle position, and video data. The video data could not be shared in the raw form, so they were processed to extract information on the other vehicles on the road, their relative positions, and intrusion events. This information was then columnized for modeling use and further enhanced by appending road information including road type, speed limit, altitude, and grade. The test route included free-flowing traffic, highway interchanges, and construction zones, as well as low-, medium-, and high-grade sections. Individual test conditions varied by day, with advanced driver-assistance system (ADAS) features engaged or disengaged and different combined vehicle masses tested in addition to uncontrolled variables such as weather and traffic interactions.
Computational fluid simulations for wave energy converters and supporting materials from Ocean Energy's WEC Buoy TEAMER project in collaboration with Sandia National Laboratories. Each file includes images and video of simulation along with the simulation files for the case. The videos and images include air and water simulations of the buoy in 2D and 3D views. Data spreadsheets of the simulation outputs are also included in the files. The data are represented as different cases in which the wave height(Hs) and peak period(Tp) were different.
Our Co-design team is from the University of Texas, working on a Department of Energy-funded project focused on the Beaumont-Port Arthur area. As part of this project, we will be developing climate-resilient design solutions for areas of the region. More on www.caee.utexas.edu.We used a DJI Mavic 2 Pro to capture aerial photos in Beaumont-Port Arthur, TX, in February 2023, including:I. Beaumont Soccer ClubII. Corps’ Port Arthur Resident OfficeIII. Halbouty Pump Station comprises its vicinityIV. Lamar University (Including Exxon Power Plants close to Lamar Univ.)V. MLK Boulevard for aerial images of the industry and the ship channelVI. Salt Water Barrier (include some aerial images about the Big Thicket)Aerial photos taken were through DroneDeploy autonomous flight, and models were processed through the DroneDeploy engine as well. All aerial photos are in .JPG format and contained in zipped files for each location.The processed data package including 3D models, geospatial data, mappings, point clouds, and the animation video of Halbouty Pump Station has various file types:- The Adobe Suite gives you great software to open .Tif files.- You can use LASUtility (Windows), ESRI ArcGIS Pro (Windows), or Blaze3D (Windows, Linux) to open a LAS file and view the data it contains.- Open an .OBJ file with a large number of free and commercial applications. Some examples include Microsoft 3D Builder, Apple Preview, Blender, and Autodesk.- You may use ArcGIS, Merkaartor, Blender (with the Google Earth Importer plug-in), Global Mapper, and Marble to open .KML files.- The .tfw world file is a text file used to georeference the GeoTIFF raster images, like the orthomosaic and the DSM. You need suitable software like ArcView to open a .TFW file.This dataset provides researchers with sufficient geometric data and the status quo of the land surface at the locations mentioned above. This dataset could streamline researchers' decision-making processes and enhance the design as well.In October 2023, we had our follow-up data collection, including:I. Beaumont Soccer ClubII. Shipping and Receiving Center at Lamar UniversityAfter the aerial collection, we obtained aerial photos of those two locations mentioned above, as well as processed data (such as point clouds and models).
Reflection high-energy electron diffraction (RHEED) is a ubiquitous in situ molecular beam epitaxial (MBE) characterization tool. Although RHEED can be a powerful means for crystal surface structure determination, it is often used as a static qualitative surface characterization method at discrete intervals during a growth. A full analysis of RHEED data collected during the entirety of MBE growths is made possible using principle component analysis (PCA) and $\textit{k}$-means clustering to examine significant boundaries that occur in the temporal clusters grouped from RHEED data and identify statistically significant patterns. This process is applied to data from homoepitaxial SrTiO 3 growths, heteroepitaxial SrTiO 3 grown on scandate substrates, BaSnO 3 films grown on SrTiO 3 substrates, and LaNiO 3 films grown on SrTiO 3 substrates. We report this analysis may provide additional insights into the surface evolution and transitions in growth modes at precise times and depths during growth, and that video archival of an entire RHEED image sequence may be able to provide more insight and control overgrowth processes and film quality.
Efficient processing of information is crucial for the optimization of neural resources in both biological and artificial visual systems. In this paper, we study the efficiency that may be obtained via the use of a fovea. Using biologically-motivated agents, we study visual information processing, learning, and decision making in a controlled artificial environment, namely the Atari Pong video game. We compare the resources necessary to play Pong between agents with and without a fovea. Our study shows that a fovea can significantly reduce the neural resources, in the form of number of neurons, number of synapses, and number of computations, while at the same time maintaining performance at playing Pong. To our knowledge, this is the first study in which an agent must simultaneously optimize its visual system, along with its decision making and action generation capabilities. That is, the visual system is integral to a complete agent.
Two-photon lithography (TPL) is an additive manufacturing technique for fabricating three-dimensional objects with nanoscale features. A main challenge of TPL is the routine and labor-intensive task of finding suitable light dosage parameters, i.e. writing speed and laser intensity that induce photo-polymerization within a wide variety of candidate photo-curing polymers. Another challenge is the monitoring required during fabrication. In this work, we apply machine learning (ML) models to accelerate the process of identifying optimal light dosage parameters and automate the detection of part quality. We curate TPL videos of different parts fabricated under a range of light dosage parameters using different resins and train spatial-temporal ML models on this data. Our results show that ML models can detect TPL part quality with a 95.1% accuracy in milliseconds. We also evaluate classification failures and identify two operating modes: parameter optimization and part quality detection. Last but not least, we publicly release this labelled dataset so that it may serve as a useful benchmark to the community. Our approach to process optimization and part quality detection addresses important aspects of TPL industrialization, is applicable beyond TPL and should benefit other additive manufacturing techniques with similar barriers to operating at industrial scale.
This is a presentation on the Role of Fluid and Temperature in Fracture Mechanics and Couples THMC Processes for Enhanced Geothermal Systems by Purdue University, presented by Laura Pyrak-Nolte. This video slide presentation describes the development and validation of a macroscopic model that can account for local deformation/friction behavior, seismic/aseismic behavior, chemical reactions, and determine the adequacy of classic Coulomb failure vs. rate-and-state friction. This presentation was featured in the Utah FORGE R&D Annual Workshop on August 13, 2024.
This is a presentation on the Determination and Modeling-Informed Analysis of Thermo-poromechanical Response of Fractured Rock for Application to FORGE by the University of Oklahoma, presented by Ahmad Ghassemi. This video presentation discusses how to improve understanding and control of coupled thermoporomechanical (or thermo-hydro-mechanical-THM) processes in reservoir development, and to demonstrate its role in interpretations of the fracture closure pressure. This presentation was featured in the Utah FORGE R&D Annual Workshop on August 13-15, 2024.
Microcracking and residual stresses are key limitations of laser powder bed fusion (LPBF), an additive manufacturing (AM) technique that uses a high power laser to consolidate successive layers of metal powder. For tungsten, these microcracks find their origin in the combination of a ductile-to-brittle transition (DBT) between 200°C and 400°C and high residual stresses. This work utilized in situ high speed video to capture the cracking mechanisms, and combined the experimental results with thermomechanical modeling to unveil correlations between crack network morphology and process parameter-related variables, such as the local temperature and residual stress distributions. Consequently, preheating and alloying with rare earth oxides were adopted as possible crack mitigation strategies, the efficacy of which was tested against the initial fundamental baseline results. Preheating temperatures above 500°C eliminated all cracking, though this threshold is expected to be higher when tungsten powder (higher oxygen content) is introduced. Alloying can serve as an active oxygen getter in the system, but the rare earth oxides in this work were too large to have significant contributions to crack-mitigation.
This training video provides an essential guide to understanding and utilizing Tamper Indicating Devices (TIDs), with a specific focus on a common Mylar (Adhesive) variety during the application process. TIDs are a critical part of nuclear security, used to detect unauthorized access to sensitive materials or facilities. The Mylar (adhesive) TID employes a tamper-evident film integrated into the TID that visually indicates any tampering attempts. This video will explain how an example Mylar (adhesive) TID works, demonstrate proper installation techniques, and highlight how to interpret its indicators to ensure the security of nuclear materials.
Full-field ultra-high-speed (UHS) X-ray imaging experiments have been well established to characterize various processes and phenomena. However, the potential of UHS experiments through the joint acquisition of X-ray videos with distinct configurations has not been fully exploited. In this paper, we investigate the use of a deep learning-based spatio-temporal fusion (STF) framework to fuse two complementary sequences of X-ray images and reconstruct the target image sequence with high spatial resolution, high frame rate and high fidelity. We applied a transfer learning strategy to train the model and compared the peak signal-to-noise ratio (PSNR), average absolute difference (AAD) and structural similarity (SSIM) of the proposed framework on two independent X-ray data sets with those obtained from a baseline deep learning model, a Bayesian fusion framework and the bicubic interpolation method. The proposed framework outperformed the other methods with various configurations of the input frame separations and image noise levels. With three subsequent images from the low-resolution (LR) sequence of a four times lower spatial resolution and another two images from the high-resolution (HR) sequence of a 20 times lower frame rate, the proposed approach achieved average PSNRs of 37.57 dB and 35.15 dB, respectively. When coupled with the appropriate combination of high-speed cameras, the proposed approach will enhance the performance and therefore the scientific value of UHS X-ray imaging experiments.
This is a presentation on the Evolution of Permeability and Strength Recovery of Shear Fractures Under Hydrothermal Conditions by United States Geological Survey, presented by Tamara Jeppson. This video slide presentation, by the USGS, discusses the determination of how thermal, hydrologic, mechanical, and chemical (THMC) processes affect the sustainability of fracture networks in geothermal reservoirs. This includes (1) the qualification of rates of change of fracture properties, (2) the parameterization of modes of reaction, (3) the development of micromechanical and empirical fracture models, and (4) extended THMC models for laboratory- and reservoir-scale models. This presentation was featured in the Utah FORGE R&D Annual Workshop on August 15, 2024.
A polarization imaging camera, Salsa (Bossa Nova Technologies) was evaluated for use in nuclear safeguards inspection with a focus on swipe sampling. The camera and supporting electronics and software determine the full Stokes parameters for the entire image field. The camera can be used for real-time video or still images. Stokes parameters S0, S1, S2, and S3 can be recorded for each pixel and exported for post processing. This study compared color photography to polarization imaging for powders on many surfaces under ambient lighting. False color images can be created from the Stokes parameters directly from the vendor software to highlight the various polarization states. In most cases, linear polarization showed the greatest effects. The polarization images are easy to collect and very sensitive to surfaces and textures. Fine powders are highlighted in the images, and particle size and refractive index effects were noticed. The images are not affected by ambient lighting (angle, or intensity). Lens choice is key and may require expert knowledge. Very small deposits of 1 µm particles were difficult to image using the standard video lens. General dirt on smooth surfaces and handprints are easily documented as well as powders on textured painted surfaces. Scratches on metal surfaces can be readily separated from other image features. The camera may be useful for tamper indication as surface damage is easy to discern and ambient lighting is not a concern.
Glass-to-metal (GtM) seals are hermetic barriers between glass and metal components, often used in the electronic and vacuum industries. Creating the seals traditionally requires heating all the components to the glass melting temperature, where the glass will flow and bond to the metal. Due to the manufacturing constraints of the individual components and sealing conditions there are geometric and material restrictions. Additive manufacturing techniques were used to make GtM seals to reduce these restrictions. Hermetic, single pin seals were produced using printed metal shells, by selective laser melting (SLM), and digital light processing (DLP) printed glass preforms. Glass preforms were printed with photosensitive resin mixed with a silicate sealing glass powder in a 1:2 weight ratio. Thermal analysis, TGA/DSC, and screening experiments were used to determine an organic removal schedule. Video techniques determined a sintering schedule by producing a densification curve at different isothermal holds which were fit with a viscous sintering model. Single pin seals were also produced use a laser assisted manufacturing (LAM) process as an alternative to the conventional furnace sealing process. A Nd:YAG laser was focused on a solid glass preform, heating the glass to form the glass-metal bond. The sealing glass was doped with a transition metal oxide, Fe2O3 or CuO, to increase absorption of the laser wavelength. The interface of hermetic LAM seals were analyzed using electron microscopy, compared to conventional prepared seals, finding a reduction of interfacial morphologies.
Droplet-on-demand liquid metal jetting is emerging as a powerful technology for the additive manufacturing of metallic parts. The success of this method hinges on overcoming several technological challenges. Additionally, the principal one among these challenges is the controlled repeatable ejection of single uniform droplets. Due to the high density and surface tension of liquid metals, the droplet ejection process occurs near the minimal extremes of the printability phase diagram, defined by acceptable ranges for the Weber (We) and Ohnesorge (Oh) numbers. In this work, we experimentally demonstrate the satellite-free ejection of pneumatically actuated molten tin droplets in this extreme corner of printability and use a combination of high-speed video analysis and volume-of-fluid modeling to elucidate the droplet dynamics. While the simulations at low Oh and We can correctly describe several aspects of the breakup process, such as an increasing tail and pinch-point near the nozzle, no single parameter set can completely capture the droplet shape at breakup. Instead, the experimental droplet dynamics appear to include features from both high and low Oh breakup. This disagreement is ascribed to the incomplete description of the droplet ejection process including wetting and exit effects near the nozzle opening and surface effects such as transient cooling and oxide formation.
Accurate estimation of delay is crucial for efficient traffic signal operations. Estimation of delay in the real-time manner using traditional loop detectors requires advanced detectors (in addition to stop-bar detection). In cases when this detection layout is not in place, delay estimates are approximated with a lower accuracy. Video detection is one of the most frequently deployed detection systems at signalized intersections in recent years. In most cases video detection operates in the same way as traditional inductive loops. However, when coupled with computer vision algorithms, video detection systems could be used to retrieve additional information (e.g., vehicular arrivals and departures) that cannot be taken out from the conventional systems (e.g., long stop-bar loop detectors). Although present for several decades, video detection data were not frequently examined for delay estimation purposes. In this study, we proposed a novel delay estimation model which can be developed with only data from stop-bar video detectors. Relevant data were collected from a simulation model of 11 signalized intersections at downtown Chattanooga, TN and processed to create needed inputs for model development. With the use of multigene genetic programming the authors developed a delay model that outperforms accuracy of multi regression model. Furthermore, authors evaluated the developed model by comparison with the other benchmark delay models, such as HCM and approach delay model. It was found that the developed MGGP delay model outperforms benchmark models for a wide range of traffic and signal operation conditions.
As aging facilities across the DOE complex await decommissioning, there is an ongoing need to understand any changes in the structural conditions. Many of these facilities were built over 50 years ago and, in some cases, these facilities have gone beyond the expected operational lifetime. Many facilities have been placed in a state of 'cold and dark,' sitting unused and awaiting decommissioning. Especially challenging are the aging facilities that provide unique operational/production capabilities to support critical DOE missions and cannot be shut down. In any of these scenarios, the structural integrity of these facilities may become compromised as time passes. It is critical that adequate inspections be performed on a continual basis and that the data collected undergoes sufficient analysis to support timely identification of any new or worsening structural issues as well as prompt needed maintenance and repairs to maintain the facilities in a safe condition. In recent days, Artificial Intelligence (AI) [1] and its application to various domains are growing at fast speed. FIU is performing research in this area and exploring the associated technologies to solve nuclear decommissioning problems. Artificial intelligence refers to the capability of a program to autonomously act, react and adapt to the working environment. AI enables the machine to behave like humans and perform the cognitive functions such as 'learning' and 'problem solving'. AI systems gradually moving from traditional approaches (algorithms and expert systems) towards more efficient and advanced technologies (machine learning [1] and deep learning [2] [3]). AI is the study of algorithms and statistical models that is being used by computers to perform specific tasks without using explicit instructions. FIU is working to develop a pilot-scale infrastructure to implement structural health monitoring using AI technologies with focus on machine learning, deep learning. This research is focused on Computer Vision/Image Classification area of AI applications. This can also be expanded to other areas of AI related to Object Recognition and Character Recognition in images. In addition to utilizing existing data sets, FIU will collect and investigate image and video data using FIU test-bed mockups to monitor structural health of the facility. Resulting data will be processed and analyzed using machine learning/deep learning technologies. The proposed pilot system is intended to serve as a starting point to engage the DOE field sites on related data sets and their decision making needs. It is anticipated that proposed machine learning/deep learning technologies can be effectively employed using anomaly detection to solve EM challenges in surveillance and maintenance of the D and D facilities. FIU will work with research stakeholders to identify applications at various sites and other DOE facilities. (authors)