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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 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Performance-Portable Particle Advection with VTK-m

Particle advection is the fundamental kernel behind most vector field visualization methods. Yet, the efficient parallel computation of large amounts of particle traces remains challenging. This is exacerbated by the variety of hardware trends in today's HPC arena, including increasing core counts in classical CPUs, many-core designs such as the Intel Xeon Phi, and massively parallel GPUs. The dedicated optimization of a particle advection kernel for each individual target architecture is both time-consuming and error prone. In this paper, we propose a performance-portable algorithm for particle advection. Our algorithm is based on the recently introduced VTK-m system and chiefly relies on its device adapter abstraction. We demonstrate the general portability of our implementation across a wide variety of hardware. Finally, our evaluation shows that our hardware-agnostic algorithm has comparable performance to hardware-specific algorithms.

Pugmire, Dave↗
DOE OSTI2024

YOLO2U-Net: Detection-guided 3D instance segmentation for microscopy

Microscopy imaging techniques are instrumental for characterization and analysis of biological structures. As these techniques typically render 3D visualization of cells by stacking 2D projections, issues such as out-of-plane excitation and low resolution in the z-axis may pose challenges (even for human experts) to detect individual cells in 3D volumes as these non-overlapping cells may appear as overlapping. In this paper a comprehensive method for accurate 3D instance segmentation of cells in the brain tissue is introduced. The proposed method combines the 2D YOLO detection method with a multi-view fusion algorithm to construct a 3D localization of the cells. Next, the 3D bounding boxes along with the data volume are input to a 3D U-Net network that is designed to segment the primary cell in each 3D bounding box, and in turn, to carry out instance segmentation of cells in the entire volume. The promising performance of the proposed method is shown in comparison with current deep learning-based 3D instance segmentation methods.

3D instance segmentation↗
DOE OSTI2020

Metrology of Time-Domain Soft X-Ray Attosecond Pulses and Reevaluation of Pulse Durations of Three Recent Experiments

Attosecond pulses in the soft x-ray (SXR) to water-window energy region offer the tools for creating and studying target-specific localized inner-shell electrons or holes in materials, enabling monitoring or controlling charge and energy flows in a dynamic system on attosecond timescales. Recently, a number of laboratories have reported generation of continuum harmonics in the hundred-electron-volt to kilovolt region with few-cycle long-wavelength mid-infrared lasers. These harmonics have the bandwidth to support pulses with durations of a few to a few-ten attoseconds. But harmonics generated in a gas medium have attochirps that cannot be fully compensated by materials over a broad spectral range; thus, realistically what are the typical shortest attosecond pulses that one can generate? To answer this question, it is essential that the temporal attosecond pulses be accurately characterized. By reanalyzing the soft x-ray attosecond metrology reported in three recent experiments using a newly developed broadband phase retrieval algorithm, we demonstrate here that the generated attosecond pulses in the first two papers have durations of about 60 as, longer than what they have reported. Similarly, the duration from the third experiment is retrieved to be about half of the 322-as upper limit cited in that work. We also introduce the autocorrelation (AC) of the streaking spectrogram. By comparing the ACs from the experiments and from the retrieved SXR pulses, the accuracy of the retrieved results can be directly visualized. Our retrieval method is fast and accurate, and it shall provide a powerful tool for the metrology of the emerging few-ten-attosecond pulses.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2025

ARCH: Large-scale knowledge graph via aggregated narrative codified health records analysis

Objective: Electronic health record (EHR) systems contain a wealth of clinical data stored as both codified data and free-text narrative notes (NLP). The complexity of EHR presents challenges in feature representation, information extraction, and uncertainty quantification. Here, to address these challenges, we proposed an efficient Aggregated naRrative Codified Health (ARCH) records analysis to generate a large-scale knowledge graph (KG) for a comprehensive set of EHR codified and narrative features. Methods: Using data from 12.5 million Veterans Affairs patients, ARCH first derives embedding vectors and generates similarities along with associated p-values to measure the strength of relatedness between clinical features with statistical certainty quantification. Next, ARCH performs a sparse embedding regression to remove indirect linkage between features to build a sparse KG. Finally, ARCH was validated on various clinical tasks, including detecting known relationships between entity pairs, predicting drug side effects, disease phenotyping, as well as sub-typing Alzheimer’s disease patients. Results: ARCH produces high-quality clinical embeddings and KG for over 60,000 codified and narrative EHR concepts. The KG and embeddings are visualized in the R-shiny powered web-API.3 ARCH achieved high accuracy in detecting EHR concept relationships, with AUCs of 0.926 (codified) and 0.861 (NLP) for similar EHR concepts, and 0.810 (codified) and 0.843 (NLP) for related pairs. It detected drug side effects with a 0.723 AUC, which improved to 0.826 after fine-tuning. Using both codified and NLP features, the detection power increased significantly. Compared to other methods, ARCH has superior accuracy and enhances weakly supervised phenotyping algorithms’ performance. Notably, it successfully categorized Alzheimer’s patients into two subgroups with varying mortality rates. Conclusion: The proposed ARCH algorithm generates large-scale high-quality semantic representations and knowledge graph for both codified and NLP EHR features, useful for a wide range of predictive modeling tasks.

Electronic health records↗
DOE OSTI2026

Methods development towards automated, physics-informed, quantitative quality control of TRISO-SiC

Tristructural-isotropic (TRISO) fuel particles have been developed as a high-performance fuel for use in high-temperature gas-cooled reactor (HTGR) systems due to their high efficiency and stability under both normal and off-normal conditions. Broader deployment of this technology in advanced nuclear applications may benefit from quantitative quality assurance and quality control (QA/QC) methods that directly link TRISO properties to downstream performance. A key TRISO property is the SiC layer microstructure, which influences fission product retention during irradiation. However, existing QA/QC for the TRISO-SiC microstructure comprises only a qualitative visual inspection; therefore, there is a clear opportunity for the development of quantitative methods for TRISO QA/QC. Here, to this end, previous work has demonstrated an image processing approach to grain boundary (GB) identification and subsequent extraction of microstructural metrics; however, extensive twinning within the SiC layer complicates such analyses because twin GBs significantly influence microstructural metrics but are not expected to contribute to fission product transport. This study presents the initial development, training, and testing of an ML-based image segmentation algorithm designed to identify and remove twin GBs from standard backscattered electron micrographs, providing an industrially applicable, quantitative, and physically meaningful QA/QC approach for the TRISO-SiC microstructure. Although pixel-wise performance metrics for the twin predictions are low, the change in grain area and the number of GB pixels after twin removal predicted by the ML workflow are within 1% of the true values calculated using crystallographic data. This suggests that the model is well capable of predicting overall twin boundary structures and grain morphology, and continued advancement of this approach could enable automated, scalable, and physics-informed QA/QC for TRISO-SiC microstructures, supporting the reliable qualification of coated particle fuels for next-generation reactor systems.

Computer vision↗
DOE OSTI2016

Development of a mobile toolmark characterization/comparison system [Development of a mobile, automated toolmark characterization/comparison system]

Since the development of the striagraph, various attempts have been made to enhance forensic investigation through the use of measuring and imaging equipment. This study describes the development of a prototype system employing an easy-to-use software interface designed to provide forensic examiners with the ability to measure topography of a toolmarked surface and then conduct various comparisons using a statistical algorithm. Acquisition of the data is carried out using a portable 3D optical profilometer, and comparison of the resulting data files is made using software named “MANTIS” (Mark and Tool Inspection Suite). The system has been tested on laboratory-produced markings that include fully striated marks (e.g., screwdriver markings), quasistriated markings produced by shear-cut pliers, impression marks left by chisels, rifling marks on bullets, and cut marks produced by knives. Using the system, an examiner has the potential to (i) visually compare two toolmarked surfaces in a manner similar to a comparison microscope and (ii) use the quantitative information embedded within the acquired data to obtain an objective statistical comparison of the data files. Finally, this study shows that, based on the results from laboratory samples, the system has great potential for aiding examiners in conducting comparisons of toolmarks.

47 OTHER INSTRUMENTATION↗
DOE OSTI2016

The Python ARM Radar Toolkit (Py-ART), a library for working with weather radar data in the Python programming language

The Python ARM Radar Toolkit is a package for reading, visualizing, correcting and analysing data from weather radars. Development began to meet the needs of the Atmospheric Radiation Measurement Climate Research Facility and has since expanded to provide a general-purpose framework for working with data from weather radars in the Python programming language. The toolkit is built on top of libraries in the Scientific Python ecosystem including NumPy, SciPy, and matplotlib, and makes use of Cython for interfacing with existing radar libraries written in C and to speed up computationally demanding algorithms. As a result, the source code for the toolkit is available on GitHub and is distributed under a BSD license.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Synchrotron‐source micro‐x‐ray computed tomography for examining butterfly eyes

Comparative anatomy is an important tool for investigating evolutionary relationships among species, but the lack of scalable imaging tools and stains for rapidly mapping the microscale anatomies of related species poses a major impediment to using comparative anatomy approaches for identifying evolutionary adaptations. We describe a method using synchrotron source micro-x-ray computed tomography (syn-μXCT) combined with machine learning algorithms for high-throughput imaging of Lepidoptera (i.e., butterfly and moth) eyes. Our pipeline allows for imaging at rates of ~15 min/mm 3 at 600 nm 3 resolution. Image contrast is generated using standard electron microscopy labeling approaches (e.g., osmium tetroxide) that unbiasedly labels all cellular membranes in a species-independent manner thus removing any barrier to imaging any species of interest. To demonstrate the power of the method, we analyzed the 3D morphologies of butterfly crystalline cones, a part of the visual system associated with acuity and sensitivity and found significant variation within six butterfly individuals. Despite this variation, a classic measure of optimization, the ratio of interommatidial angle to resolving power of ommatidia, largely agrees with early work on eye geometry across species. We show that this method can successfully be used to determine compound eye organization and crystalline cone morphology. Our novel pipeline provides for fast, scalable visualization and analysis of eye anatomies that can be applied to any arthropod species, enabling new questions about evolutionary adaptations of compound eyes and beyond.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Microreactor Automated Control System - Digital Twin Models and Advanced Control Systems Updates

Automation of control systems is expected to be important in the economic and safe operation of microreactors. Therefore, there is a need to develop and demonstrate automated control for microreactors, along with the development of testbeds for this purpose. This report provides updates on the status of a nonnuclear microreactor automated control system (MACS)—a real-time, hardware-in-the-loop testbed for non-nuclear testing of microreactor control system automation. A real-time hardware-in-the-loop testbed incorporates the realistic dynamics of physical systems into control system development and testing. The collaborative effort between Oak Ridge National Laboratory (ORNL) and Idaho National Laboratory (INL) resulted in the development of a prototypic microreactor plant-level digital twin that includes the reactor and a balance of plant system. Advanced control strategies were incorporated to demonstrate testing of control automation solutions. The gRPC communication protocol, which was implemented in the hardware-in-the-loop testbed by INL, was coupled to a digital twin model developed using the TRANsient Simulation Framework of Reconfigurable Models (TRANSFORM) library in Modelica. This digital twin simulation was tested with the ViBRANT hardware for realistic feedback and visual representation of control action in real time. A modular Python client structure was developed to manage functional mock-up unit-based simulation and real-time gRPC communication. Hardware-in-the-loop testing indicated that the modeled reactor—a natural-convection, molten-salt coolant loop configuration—responds well to control of drum positioning for modulation of reactor core power, as well as system-level control and downstream demand changes. Ongoing research is focused on integrating additional control algorithms that utilize data from newly included sensors within the MACS hardware testbed, as well as demonstrating and assessing the performance of the different automated control algorithms on multiple additional operational scenarios.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

From 2D to 4D: a containerized workflow and browser to explore dynamic chromatin architecture

Background Characterizing the physical organization of the genome is essential for understanding long-range gene regulation, chromatin compartmentalization, and epigenetic accessibility. Hi-C experiments generate two-dimensional (2D) genome-wide contact maps of chromatin interactions by capturing the spatial proximity between genomic loci, which reveal interaction frequencies but lack the spatial resolution needed to interpret the three-dimensional (3D) genome structure(s). Emerging evidence suggests that epigenetic regulation is closely linked to 3D genome architecture, and that structural changes over time (4D) drive key biological processes in development, disease, and environmental response. Thus, integrating 3D structure with functional data is critical for a more complete understanding of genome regulation. Previous work, most notably the 4DHiC chromosome modeling framework, has shown that physical multi-dimensional modeling approaches rooted in polymer physics and molecular dynamics can resolve these structures at biologically meaningful resolutions by integrating temporal Hi-C data with physical constraints to uncover dynamic chromosome reorganization. Thus, molecular dynamics simulations, constrained by Hi-C contact matrices, can resolve fine-scale structural changes and reveal functionally significant transitions in chromatin conformation. Results Herein, we present the 4D Genome Browser Workflow (4DGBWorkflow) and the 4D Genome Browser (4DGB). The algorithm is based on the 4DHiC method, and the containerized tool is an end-to-end workflow that can transform, filter, and view 4D epigenomics and chromatin datasets, allowing non-specialists to apply three-dimensional modeling principles to diverse datasets and experimental conditions. The software executes on a laptop running macOS, Linux or Windows. From input Hi-C files (.hic), the 4DGBWorkflow produces 3D reconstructions of chromosomes, integrates the reconstruction with track data (e.g., epigenetic marks, transcriptome profiles), and provides comparative visualization of the results in a single workflow. Conclusions The 4DGBWorkflow and 4D Genome Browser are open-source tools for comparative analysis and visualization of 4D chromosome datasets, including chromatin architecture and epigenomic signals. Automatic integration of Hi-C data with molecular dynamics democratizes the construction of time resolved 3D genome structures, simplifying complex simulations and data integration schemes.

3D Genome Browser↗
DOE OSTI2021

TracKlinic: Diagnosis of Challenge Factors in Visual Tracking

Generic visual object tracking is difficult due to many challenge factors (e.g., occlusion, blur, etc.). Each of these factors may cause serious problems for a tracker, and when they work together can make things even more complicated. Despite a great amount of efforts devoted to understanding the behavior of trackers, reliable and quantifiable ways for studying the per factor tracking behavior remain barely available. Addressing this issue, in this paper we contribute to the community a tracking diagnosis toolkit, TracKlinic, for diagnosis of challenge factors of tracking algorithms. TracKlinic consists of two novel components focusing on the data and analysis aspects, respectively. For the data component, we carefully prepare a set of 2,390 annotated videos, each involving one and only one major challenge factor. When analyzing an algorithm for a specific challenge factor, such one-factor-per-sequence rule greatly inhibits the disturbance from other factors and consequently leads to more faithful analysis. For the analysis component, given the tracking results on all sequences, it investigates the behavior of the tracker under each individual factor and generates the report automatically. With TracKlinic, a thorough study is conducted on ten state-of-the-art trackers on nine challenge factors (including two compound ones). The results suggest that, heavy shape variation and occlusion are the two most challenging factors faced by most trackers. Besides, out-of-view, though does not happen frequently, is often fatal. By sharing TracKlinic1, we expect to make it much easier for diagnosing tracking algorithms, and to thus facilitate developing better ones.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Eastern North Atlantic (ENA) Aerosol Supplementary Site (S1) Data Analysis Report

We present strategies to identify time periods when local aerosol sources impact the data collected at the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility in the Eastern North Atlantic (ENA). Data from the aerosol observing system (AOS) at the Central Facility and a temporary aerosol Supplementary Site (S1) were evaluated. S1 instrumentation was deployed within 0.75 km of the ENA Central Facility (C1) for approximately one year. Two time periods, one during the summer and one during the winter, were examined in support of the Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) intensive operating periods (IOPs) in 2017 and 2018. As the criteria for identifying regionally representative data varies based on the specific needs of the user, we present and discuss different strategies using measured aerosol properties and their associated meteorological parameters to isolate periods in AOS data that were impacted by local aerosol at ENA. We identify high-concentration aerosol events at ENA using submicron number concentrations, size distributions, high-time-resolution measurements, and diurnal profiles. An improved understanding of local aerosol to select the optimal aerosol mask was obtained with an understanding of local meteorology during the different seasons. We developed a mathematical algorithm that employs AOS data to mask high-concentration aerosol events at ENA. We validated the ENA Aerosol Mask with aerosol data from C1 and S1 and associated meteorological data. The Supplementary Site was ideal to understand and constrain local aerosol sources within the region. In its absence, collocated data from the ENA AOS could be used to select appropriate parameters to develop an aerosol mask that would limit over- or underestimating the regional baseline aerosol data. When available, associated metadata, such as airport flight logs and visual identification with AOS cameras should also be used to validate the application of an aerosol mask at a new site. Our results confirm the importance of optimizing an aerosol mask prior its application. In the future, the application of an aerosol mask at ARM observatories and during mobile deployments could be used to increase the data quality of continuous AOS data and to reduce uncertainties when parameterizing regional aerosol processes and impacts on clouds for incorporation in models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Image fusion using sparse overcomplete feature dictionaries

Approaches for deciding what individuals in a population of visual system "neurons" are looking for using sparse overcomplete feature dictionaries are provided. A sparse overcomplete feature dictionary may be learned for an image dataset and a local sparse representation of the image dataset may be built using the learned feature dictionary. A local maximum pooling operation may be applied on the local sparse representation to produce a translation-tolerant representation of the image dataset. An object may then be classified and/or clustered within the translation-tolerant representation of the image dataset using a supervised classification algorithm and/or an unsupervised clustering algorithm.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

FECM/NETL Natural Gas with Hydrogen Pipeline Cost Model (2024): Description and User’s Manual

This is the user’s manual for The FECM/NETL Natural Gas with Hydrogen Pipeline Cost Model (NG-H2_P_COM) that estimates costs for transporting gaseous hydrogen with natural gas in a pipeline from a source, such as a hydrogen production facility, to a final destination which may be a user of the hydrogen and natural gas or a distribution center where hydrogen in the pipeline with natural gas is diverted to multiple end users. This user’s manual provides two main functions. First, the detailed statement describes the equations and algorithms that are used by the model to calculate technical quantities (such as blend hydrogen percentage, reuse percentage of the pipeline and stations, the pipe diameter size and length needed to transport a user-specified hydrogen with natural gas rate in a specified distance) and engineering-economic quantities (such as capital costs, operating costs, and cash flows). Second, the document is a user’s manual for the model that describes the procedures the user must follow to configure and setup the model, run the model, analyze the results, and visualize the outcomes. Such details offer user a quick and handy way to utilize the model for their application and decision making. The model can be accessed at this URL: https://www.netl.doe.gov/energy-analysis/details?id=cf3f6564-3c55-4aa5-b712-7160e558d9f6. The Model Results and Comparative Analysis can be accessed here: https://www.netl.doe.gov/energy-analysis/details?id=83862799-a28c-4944-a809-90b7e23d4af6.

03 NATURAL GAS↗
DOE OSTI2024

RG-CAT: Detection pipeline and catalogue of radio galaxies in the EMU pilot survey

Abstract We present source detection and catalogue construction pipelines to build the first catalogue of radio galaxies from the 270$\rm deg^2$pilot survey of the Evolutionary Map of the Universe (EMU-PS) conducted with the Australian Square Kilometre Array Pathfinder (ASKAP) telescope. The detection pipeline uses Gal-DINO computer vision networks (Gupta et al. 2024, PASA, 41, e001) to predict the categories of radio morphology and bounding boxes for radio sources, as well as their potential infrared host positions. The Gal-DINO network is trained and evaluated on approximately 5 000 visually inspected radio galaxies and their infrared hosts, encompassing both compact and extended radio morphologies. We find that the Intersection over Union (IoU) for the predicted and ground-truth bounding boxes is larger than 0.5 for 99% of the radio sources, and 98% of predicted host positions are within$3^{\prime \prime}$of the ground-truth infrared host in the evaluation set. The catalogue construction pipeline uses the predictions of the trained network on the radio and infrared image cutouts based on the catalogue of radio components identified using theSelavysource finder algorithm. Confidence scores of the predictions are then used to prioritiseSelavycomponents with higher scores and incorporate them first into the catalogue. This results in identifications for a total of 211 625 radio sources, with 201 211 classified as compact and unresolved. The remaining 10 414 are categorised as extended radio morphologies, including 582 FR-I, 5 602 FR-II, 1 494 FR-x (uncertain whether FR-I or FR-II), 2 375 R (single-peak resolved) radio galaxies, and 361 with peculiar and other rare morphologies. Each source in the catalogue includes a confidence score. We cross-match the radio sources in the catalogue with the infrared and optical catalogues, finding infrared cross-matches for 73% and photometric redshifts for 36% of the radio galaxies. The EMU-PS catalogue and the detection pipelines presented here will be used towards constructing catalogues for the main EMU survey covering the full southern sky.

Astronomy & Astrophysics↗
DOE OSTI2016

VERA and VERA-EDU 3.5 Release Notes

The Virtual Environment for Reactor Applications components included in this distribution include selected computational tools and supporting infrastructure that solve neutronics, thermal-hydraulics, fuel performance, and coupled neutronics-thermal hydraulics problems. The infrastructure components provide a simplified common user input capability and provide for the physics integration with data transfer and coupled-physics iterative solution algorithms. Neutronics analysis can be performed for 2D lattices, 2D core and 3D core problems for pressurized water reactor geometries that can be used to calculate criticality and fission rate distributions by pin for input fuel compositions. MPACT uses the Method of Characteristics transport approach for 2D problems. For 3D problems, MPACT uses the 2D/1D method which uses 2D MOC in a radial plane and diffusion or SPn in the axial direction. MPACT includes integrated cross section capabilities that provide problem-specific cross sections generated using the subgroup methodology. The code can be executed both 2D and 3D problems in parallel to reduce overall run time. A thermal-hydraulics capability is provided with CTF (an updated version of COBRA-TF) that allows thermal-hydraulics analyses for single and multiple assemblies using the simplified VERA common input. This distribution also includes coupled neutronics/thermal-hydraulics capabilities to allow calculations using MPACT coupled with CTF. The VERA fuel rod performance component BISON calculates, on a 2D or 3D basis, fuel rod temperature, fuel rod internal pressure, free gas volume, clad integrity and fuel rod waterside diameter. These capabilities allow simulation of power cycling, fuel conditioning and deconditioning, high burnup performance, power uprate scoping studies, and accident performance. Input/Output capabilities include the VERA Common Input (VERAIn) script which converts the ASCII common input file to the intermediate XML used to drive all of the physics codes in the VERA Core Simulator (VERA-CS). VERA component codes either input the VERA XML format directly, or provide a preprocessor which can convert the XML into native input. VERAView is an interactive graphical interface for the visualization and engineering analyses of output data from VERA. The python-based software is easy to install and intuitive to use, and provides instantaneous 2D and 3D images, 1D plots, and alpha-numeric data from VERA multi-physics simulations. Testing within CASL has focused primarily on Westinghouse four-loop reactor geometries and conditions with example problems included in the distribution.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2016

WE-AB-207A-12: HLCC Based Quantitative Evaluation Method of Image Artifact in Dental CBCT

Purpose: Image artifacts are usually evaluated qualitatively via visual observation of the reconstructed images, which is susceptible to subjective factors due to the lack of an objective evaluation criterion. In this work, we propose a Helgason-Ludwig consistency condition (HLCC) based evaluation method to quantify the severity level of different image artifacts in dental CBCT. Methods: Our evaluation method consists of four step: 1) Acquire Cone beam CT(CBCT) projection; 2) Convert 3D CBCT projection to fan-beam projection by extracting its central plane projection; 3) Convert fan-beam projection to parallel-beam projection utilizing sinogram-based rebinning algorithm or detail-based rebinning algorithm; 4) Obtain HLCC profile by integrating parallel-beam projection per view and calculate wave percentage and variance of the HLCC profile, which can be used to describe the severity level of image artifacts. Results: Several sets of dental CBCT projections containing only one type of artifact (i.e. geometry, scatter, beam hardening, lag and noise artifact), were simulated using gDRR, a GPU tool developed for efficient, accurate, and realistic simulation of CBCT Projections. These simulated CBCT projections were used to test our proposed method. HLCC profile wave percentage and variance induced by geometry distortion are about 3∼21 times and 16∼393 times as large as that of the artifact-free projection, respectively. The increase factor of wave percentage and variance are 6 and133 times for beam hardening, 19 and 1184 times for scatter, and 4 and16 times for lag artifacts, respectively. In contrast, for noisy projection the wave percentage, variance and inconsistency level are almost the same with those of the noise-free one. Conclusion: We have proposed a quantitative evaluation method of image artifact based on HLCC theory. According to our simulation results, the severity of different artifact types is found to be in a following order: Scatter>Geometry>Beam hardening>Lag>Noise>Artifact-free in dental CBCT.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Reinforcement Learning-based Traffic Control to Optimize Energy Usage and Throughput (CRADA report)

The US roadways are critical to meeting the mobility and economic needs of the nation. The United States uses 28% of its energy in moving goods and people, with approximately 60% of that utilized by cars, light trucks, and motorcycles. Thus, improved transportation efficiency is vital to America’s economic progress. The increasing congestion and energy resource requirements of transportation systems for metropolitan areas require research in methods to improve and optimize control methods. Coordinating and optimizing traffic in urban areas may introduce hundreds of thousands of vehicles and traffic management systems, which can require high performance computing (HPC) resources to model and manage. In this work, we seek to use machine learning, computer vision, and HPC to improve the energy efficiency aspects of traffic control by leveraging GRIDSMART traffic cameras as sensors for adaptive traffic control, with a sensitivity to the fuel consumption characteristics of the traffic in the camera’s visual field. Traffic control use cases using reinforcement learning have been published and achieved good results. Surveys from DOE national laboratories estimate that the fuel cost of idling is 6 billion gallons wasted annually [Argonne National Laboratory, 2019]. GRIDSMART cameras—an existing, fielded commercial product—sense the presence of vehicles at intersections and replace more conventional sensors (such as inductive loops) to issue calls to traffic control. These cameras, which have horizon-to-horizon view, offer the potential for an improved view of the traffic environment which can be used to generate better control algorithms.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗