Morphological Methods for Signal Extraction and Parameter Estimation
Presentation is to be given at upcoming Reaction History Workshop, in conjunction with Lawrence Livermore National Lab and Los Alamos National Lab.
97 MATHEMATICS AND COMPUTING↗
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Results for “Image processing”
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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Film Grain Simulation for Oscilloscope Trace Image Synthesis
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99 GENERAL AND MISCELLANEOUS↗
Occlusion-Perturbed Deep Learning for Probabilistic Solar Forecasting via Sky Images: Preprint
Solar forecasting is shifting to the probabilistic paradigm due to the inherent uncertainty within the solar resource. Input uncertainty quantification is one of the widely used and best-performing ways to model solar uncertainty. However, compared to other sources of inputs, such as numerical weather prediction models, pure sky image-based probabilistic solar forecasting lags behind. In this research, an occlusion-perturbed convolutional neural network, named the PSolarNet, is developed. The PSolarNet provides very short-term deterministic forecasts, forecast scenarios, and probabilistic forecasts of the global horizontal irradiance from sky image sequences. Case studies based on 6 years of open-source data show that the developed PSolarNet is able to generate accurate 10-minute ahead deterministic forecasts with a 5.62% normalized root mean square error, realistic and diverse forecast scenarios with a 0.966 average correlation with the actual time series, and reliable and sharp probabilistic forecasts with a 2.77% normalized continuous ranked probability score.
Bayesian model averaging↗
The Impacts of Source Location in Tomographic Gamma Scanning - 17324
Canberra Industries has recently designed and built a Tomographic Gamma Scanning (TGS) system for the deployment in a nuclear facility. The TGS technique combines high-resolution gamma spectroscopy with low spatial resolution 3-dimensional image reconstruction to provide increased accuracy over traditional approaches for the assay of non-uniform source distributions in low-to medium-density containers. The TGS system has been optimized to measure 120 liter drums and has been equipped to use various collimators and attenuators to assay containers under a wide range of operational conditions. In this paper, we report on experimental work using TGS systems aimed at evaluating the effects of source locations at the limits of volume elements, or voxels, defined in the image reconstruction. These include sources placed at voxel boundaries within a single vertical layer and sources placed in the lowest and highest possible vertical positions within the containers. Building upon previous studies, the accuracy of the TGS system was also assessed by varying the radial position of a point source within a vertical segment. The robustness of the TGS image reconstruction algorithm to location of the sources location was also analyzed as a function of matrix density and gamma-ray energy. Methods to reduce the biases from source location at extreme positions within the container were implemented and tested. The results from this work have been used to better understand the total measurement uncertainty (TMU) and analysis methodology used in TGS systems currently deployed and under development. (authors)
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
3D Reconstruction, Augmented Reality, and Telerobotic Operation - 18297
For nuclear facility decommissioning and waste cleanup, it is necessary to deploy simple and robust robots in remote sites performing precise and dexterous manipulation under teleoperation. To meet the challenge, this paper presents a new remote operation method based on augmented reality for intuitive and efficient teleoperation. The enhanced teleoperation system incorporates three technology components - 3D sensing and reconstruction, augmented reality, and teleoperation. The 3D reconstruction technology utilizes 3D cameras and constructs a 3D geometric model of the environment. Compared to the state-of-the- art technology, the presented 3D reconstruction has greater spatial precision and dynamic object tracking capability, which enhances the precision of manipulation tasks in a dynamic environment. The augmented reality technology entails generation of virtual surfaces of various geometries and dynamic characteristics, as well as consistent perceptual overlay in camera, robot and operator hand control spaces. Teleoperation is demonstrated with a dual-arm robot system operated under Robot Operating System (ROS). (authors)
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Localized Imaging and Mapping for Underwater Fuel Storage Basins - 18364
Current methods for inspection of spent nuclear fuel storage basins involve lowering a single camera for visual inspection of walls and other structures. We present a localized inspection solution where the images are automatically annotated by localization information and a 3D model of the inspected area is generated. The system consists of an underwater sensor pod containing a stereo pair of cameras, light source, inertial measurement unit, and a pressure sensor. The sensors are time synchronized to provide precise measurements. We describe both the sensor pod and the algorithms that keep the pod localized. Preliminary results from in-air testing of a prototype are presented. (authors)
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
3D Structure of Oil Droplets in Hardened Geopolymer Emulsions: Impact of Various Commercial Liquid Oils - 20184
Cement-based solidification/stabilization (S/S) is a widely applied technique for the immobilization of inorganic hazardous constituents. However, S/S is considered less compatible for organic liquid waste (OLW) because organic compounds may inhibit binder hydration. Geopolymers (i.e. alumino-silicate materials) are an alternative for applications in which Portland cements are inappropriate. In a previous work, we proposed a direct immobilization of organic liquid waste in a geopolymer binder (GEOIL composite). We demonstrated the feasibility for producing various composites on model system (hexadecane) and on a hydraulic oil, which are dispersed in the geopolymer binder as droplets (10-50 μm typical size). In nuclear facilities, legacy waste based on organic liquids are currently stored. Many of them have been used for various applications in the nuclear industry (lubrication, cutting, analysis, liquid/liquid extraction..) with very wide chemical compositions. In this study, we study the influence of various commercial organic oils immobilized in hardened geopolymer emulsions by X-ray micro-tomography. Image analysis make it possible to determine the 3D spatial and size distributions of oil droplets in geopolymer emulsions. (authors)
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Automatic Determination of the Need for Intravenous Contrast in Musculoskeletal MRI Examinations Using IBM Watson’s Natural Language Processing Algorithm
Magnetic resonance imaging (MRI) protocoling can be time- and resource-intensive, and protocols can often be suboptimal dependent upon the expertise or preferences of the protocoling radiologist. Providing a best-practice recommendation for an MRI protocol has the potential to improve efficiency and decrease the likelihood of a suboptimal or erroneous study. The goal of this study was to develop and validate a machine learning-based natural language classifier that can automatically assign the use of intravenous contrast for musculoskeletal MRI protocols based upon the free-text clinical indication of the study, thereby improving efficiency of the protocoling radiologist and potentially decreasing errors. We utilized a deep learning-based natural language classification system from IBM Watson, a question-answering supercomputer that gained fame after challenging the best human players on Jeopardy! in 2011. We compared this solution to a series of traditional machine learning-based natural language processing techniques that utilize a term-document frequency matrix. Each classifier was trained with 1240 MRI protocols plus their respective clinical indications and validated with a test set of 280. Ground truth of contrast assignment was obtained from the clinical record. For evaluation of inter-reader agreement, a blinded second reader radiologist analyzed all cases and determined contrast assignment based on only the free-text clinical indication. In the test set, Watson demonstrated overall accuracy of 83.2% when compared to the original protocol. This was similar to the overall accuracy of 80.2% achieved by an ensemble of eight traditional machine learning algorithms based on a term-document matrix. When compared to the second reader’s contrast assignment, Watson achieved 88.6% agreement. When evaluating only the subset of cases where the original protocol and second reader were concordant (n = 251), agreement climbed further to 90.0%. The classifier was relatively robust to spelling and grammatical errors, which were frequent. Implementation of this automated MR contrast determination system as a clinical decision support tool may save considerable time and effort of the radiologist while potentially decreasing error rates, and require no change in order entry or workflow.
62 RADIOLOGY AND NUCLEAR MEDICINE↗
Combining In-situ Diagnostics and Data Analytics for Discovery of Process-Structure-Property Relationships in AM parts – A Step Toward Digital Twins
In-situ additive manufacturing (AM) diagnostic tools (e.g., optical/infrared imaging, acoustic, etc.) already exist to correlate process anomalies to printed part defects. This current work aimed to augment existing capabilities by: 1) Incorporating in-situ imaging w/ machine learning (ML) image processing software (ORNL- developed "Peregrine") for AM process anomaly detection 2) Synchronizing multiple in-situ sensors for simultaneous analysis of AM build events 3) Correlating in-situ AM process data, generated part defects and part mechanical properties The key R&D question investigated was to determine if these new combined hardware/software tools could be used to successfully quantify defect distributions for parts build via SNL laser powder bed fusion (LPBF) machines, aiming to better understand data-driven process-structure-property- performance relationships. High resolution optical cameras and acoustic microphones were successfully integrated in two LPBF machines and linked to the Peregrine ML software. The software was successfully calibrated on both machines and used to image hundreds of layers of multiple builds to train the ML software in identifying printed part vs powder. The software's validation accuracy to identify this aspect increased from 56% to 98.8% over three builds. Lighting conditions inside the chamber were found to significantly impact ML algorithm predictions from in-situ sensors, so these were tailored to each machine's internal framework. Finally, 3D part reconstructions were successfully generated for a build from the compressed stack of layer-wise images. Resolution differences nearest and furthest from the optical camera were discussed. Future work aims to improve optical resolution, increase process anomalies identified, and integrate more sensor modalities.
36 MATERIALS SCIENCE↗
Biological material fouling assessment systems and methods
Biological material fouling assessment systems and methods are described. According to one aspect, a biological material fouling assessment system includes processing circuitry configured to access image data of an image of a surface of a substrate which has been fouled with biological material, wherein the image data comprises intensity information regarding a plurality of pixels of the image, and wherein the processing circuitry is further configured to process the intensity information regarding the pixels of the image to generate information which is indicative of an amount of the fouling of the biological material upon the surface of the substrate.
42 ENGINEERING↗
Experimental Modal Analysis Using Phase Quantities from Phase-Based Motion Processing and Motion Magnification
Phase-based motion processing and the associated Motion Magnification that it enables has become popular not only for the striking videos that it can produce of traditionally stiff structures visualized with very large deflections, but also for its ability to pull information out of the noise floor of images so that they can be processed with more traditional optical techniques such as digital image correlation or feature tracking. While the majority of papers in the literature have utilized the Phase-based Image Processing approach as a pre-processor for more quantitative analyses, the technique itself can be used directly to extract modal parameters from an image, noting that the extracted phases are proportional to displacements in the image. Therefore, once phases are extracted, they can be fit using traditional experimental modal analysis techniques. This produces a mode “shape” where the degrees of freedom are phases instead of physical motions. These phases can be scaled to produce on-image visualizations of the mode shapes, rather than operational shapes produced by bandpass filtering. Modal filtering techniques can also be used to visualize motions from an environment on an image using the modal phases as a basis for the expansion.
47 OTHER INSTRUMENTATION↗
Layer-wise Imaging Dataset from Powder Bed Additive Manufacturing Processes for Machine Learning Applications (Peregrine v2021-03)
This dataset contains layer-wise powder bed images from three different powder bed printing technologies â laser powder bed fusion, electron beam powder bed fusion, and binder jetting. This dataset was collected and annotated using the internally-developed Peregrine software tool and is designed primarily to facilitate research into anomaly defect detection using image segmentation or similar techniques. A total of 20 layers are provided for each printing technology, with each layer of data consisting of one or more calibrated images and an annotation file containing pixel-wise ground truth labels. The ground truths were labeled by domain experts, typically printer technicians. Data in this release were collected at Oak Ridge National Laboratory between 2016 and 2020 and were compiled in March 2021.
36 MATERIALS SCIENCE↗
Initial Stage of Nanoscale Imaging in Positive Tone Extreme UV Photoresists: The Influence of the Polymer Sequence
Photolithographic patterning using extreme ultraviolet (EUV, 92.5 eV) light is a radiolytic process that initially forms electrons, radical cations, anions, and neutral radicals in the polymeric photoresist matrix. These species may participate in the chemical reactions that define the ultimate resolution of the printed image, and their concentrations and nanometer-scale stochastic variations in their formation influence printed image quality. Proposals have been made that polymer chain uniformity may be advantageous in reducing stochastics due to spatial inhomogeneities, and this aspect of radiolysis is examined in this work. We have simulated the initial subpicosecond stages of the imaging process for a series of photoresist films that are identical in composition but vary in their polymer chain structures. We use detailed, physically accurate stochastic reaction-diffusion calculations to evaluate the influence of defined sequence and random copolymer structures on radiolytic spur formation, i.e., a cluster of species formed by electron-polymer interactions that defines the initial spatial characteristic of the imaging process. Predictions of electron thermalization in the present work are shown to be consistent with the literature, indicating that our overall computational approach for ultrafast nanoscale processes is sound. The computational results show that the polymer sequence has no significant effect on the spur composition. This suggests that any potential imaging improvements to be gained by sequence control must originate from postimaging lithographic process steps.
Absorption↗
Coupled Transport, Reactivity, and Mechanics in Fractured Shale Caprocks
Abstract Shales are low‐permeability caprocks that confine fluid, such as CO 2 , nuclear waste, and hydrogen, in storage formations. Stress‐induced fractures in shale caprocks provide pathways for fluid to leak and potentially contaminate fresh water aquifers. Fractured shales are also increasingly considered as resources for CO 2 sequestration, enhanced geothermal, and unconventional energy recovery. Injecting reactive fluids into shales introduces chemical disequilibrium, causing an onset of a series of dissolution, precipitation, and fines mobilization mechanisms. The reactions have rapid kinetics and significant impact on porosity and permeability; consequently, flow and storage properties of caprocks. While previous research has explored the separate effects of these reactions, this study aims to uncover their simultaneous occurrence and collective influence. This study unveils these highly coupled transport and reactivity mechanisms by tracking and visualizing the reaction‐induced alterations in the matrix, microcracks, and fractures of shales over time. We conducted brine injection experiments sequentially at pH 4 and 2 in a naturally fractured Wolfcamp shale sample while simultaneously imaging the dynamic processes using X‐ray computed tomography (CT). CT images are validated by finer resolution images obtained using micro‐CT and scanning electron microscopy. We also tracked the sample permeability and fluid chemistry using brine permeability and inductively coupled plasma mass spectrometry, respectively. Findings show that fluid primarily flowed through fractures, dissolving reactive minerals and mobilizing fines on fracture surfaces. Dissolution of fracture asperities under confining stress resulted in the closing of fractures. Clogging in narrow fracture pathways, caused by fines accumulation, diverted fluid flow into matrix pores.
58 GEOSCIENCES↗
Fast Active-Set Thresholding Method for Nonnegative Least Squares
Nonnegative Least Squares (NNLS) is a fundamental constrained optimization problem encountered in many applications such as image deblurring, signal processing, nonnegative matrix factorization, magnetic microscopy, and hyperspectral imaging. Active-set based methods are a common class of algorithms for solving NNLS which identify the optimal variable set of the NNLS solution. They do so by iteratively solving a series of unconstrained least squares problems, identifying which variables violate the nonnegativity constraints, and then swapping variables in/out of consideration until the optimal set of variables is found. Several variations improving upon this method exist in the literature. In this work, we propose an active-set swap heuristic which further improves upon existing active-set based methods for NNLS. Our optimizations are based upon adding multiple variables to the passive set within a threshold of the smallest gradient value and removing variables within a similar threshold of the closest boundary constraint. We leverage these optimizations to yield a Fast Active-Set Thresholding NNLS (FAST-NNLS) algorithm which significantly outperforms the existing state-of-the-art NNLS algorithms for a wide range of problems. Rigorous convergence guarantees are proven for the proposed method. We demonstrate the effectiveness of our proposed method on multiple synthetic datasets and two realworld text analysis applications. In doing so, we present the most comprehensive NNLS solver comparison in the literature to date.
Cobb, Benjamin [Georgia Institute of Technology]↗
Lensless X-Ray Nanoimaging: Revolutions and opportunities
Lensless x-ray nanoimaging is providing 3D views of a wide range of materials with a spatial resolution better than 20 nanometers. These advances are enabled in part by dramatic gains in coherent x-ray flux, but they also rely on advances in signal processing to obtain images from coherent diffraction data. Here, we outline the various imaging approaches, the associated reconstruction methods, and highlight opportunities for future advances.
42 ENGINEERING↗
Data Archive and Portal (DAP) Platform for Solid Phase Processing Technologies
The scale and speed of data generated by modern scientific experiments have constantly challenged the research community to store, curate, manage and optimally use it to drive scientific discoveries. In this work, we have developed a data archive and portal (DAP) platform including analytics capabilities to collect, curate, and manage data and metadata stream for solid phase processing (SPP) techniques. We successfully hosted around ~347K files of data related to processing parameters, microscopic images, and spectroscopic data related to solid phase processing. The DAP platform for SPP will establish an enduring capability to support machine learning and grow collaboration at the intersection of materials science and data science.
36 MATERIALS SCIENCE↗
Simulation tools for atom probe tomography: A path for diagnosis and treatment of image degradation
The ideal picture of a near-perfect 3D microscope often presented regarding Atom Probe Tomography faces several issues. These issues degrade the metrological performance of the instrument and find their roots in the phenomena acting at the atomic to the mesoscopic level in the vicinity of the surface of a field emitter. From the field evaporation process at the atomic scale, to the macroscopic scale of the instrument, the path to model the imaging process and to develop more accurate and reliable reconstruction algorithms is not a single lane road. This paper focused on the numerical methods used to understand, treat, and potentially heal imaging issues commonly affecting the data in atom probe experiments. A lot of room for improvement exists in solving accuracy problems observed in complex materials by means of purely electrostatic models describing the image formation in a classical approach. Looking at the sample at the atomic scale, the phenomena perturbing the imaging process are subtle. An examination of atomic scale modifications of the sample surface in the presence of a high surface electric field is therefore mandatory. Atomic scale molecular dynamic models integrating the influence of the high surface electric are being developed with this aim. It is also demonstrated that the complex behavior of atoms and molecules in high fields, and consequences on collected data, can be understood through the use of accurate ab-initio models modified to include the impact of the extreme surface electric field.
77 NANOSCIENCE AND NANOTECHNOLOGY↗