Radiographic Image of a Toroid – uncertainty in 3D reconstruction
No abstract prepared.
97 MATHEMATICS AND COMPUTING↗
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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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INVESTIGATION OF BREAKDOWN INDUCED SURFACE DAMAGE ON 805 MHZ PILLBOX CAVITY INTERIOR SURFACES
The MuCool Test Area (MTA) at Fermilab is a facility to develop the technology required for ionization cooling for a future Muon Collider and/or Neutrino Factory. As part of this research program, we have tested two 805 MHz vacuum RF cavities in a multi-Tesla magnetic field to study the effects of the static magnetic field on the cavity operation. This study gives useful information on field emitters in the cavity, dark current, surface conditioning, breakdown mechanisms and material properties of the cavity. All these factors determine the maximum accelerating gradient in the cavity. This paper discusses the image processing technique for quantitative estimation of spark damage spot distribution on cavity interior surfaces. The distribution is compared with the electric field distribution predicted by a computer code calculation. The local spark density is proportional to probability of surface breakdown and shows a power law dependence on the maximum electric field (E). This E dependence is consistent with the dark current calculated from the Fowler-Nordheim equation.
43 PARTICLE ACCELERATORS↗
Computer systems for annotation of single molecule fragments
There are provided computer systems for visualizing and annotating single molecule images. Annotation systems in accordance with this disclosure allow a user to mark and annotate single molecules of interest and their restriction enzyme cut sites thereby determining the restriction fragments of single nucleic acid molecules. The markings and annotations may be automatically generated by the system in certain embodiments and they may be overlaid translucently onto the single molecule images. An image caching system may be implemented in the computer annotation systems to reduce image processing time. The annotation systems include one or more connectors connecting to one or more databases capable of storing single molecule data as well as other biomedical data. Such diverse array of data can be retrieved and used to validate the markings and annotations. The annotation systems may be implemented and deployed over a computer network. They may be ergonomically optimized to facilitate user interactions.
59 BASIC BIOLOGICAL SCIENCES↗
Sandia Simple Particle Tracking (Sandia SPT) v. 1.0
Sandia SPT is designed as software to accompany a book chapter being published a methods chapter which provides an introduction on how to label and track individual proteins. The Sandia Simple Particle Tracking code uses techniques common to the image processing community, where its value is that it facilitates implementing the methods described in the book chapter by providing the necessary open-source code. The code performs single particle spot detection (or segmentation and localization) followed by tracking (or connecting the detected particles into trajectories). The book chapter, which along with the headers in each file, constitutes the documentation for the code is: Anthony, S.M.; Carroll-Portillo, A.; Timlon, J.A., Dynamics and Interactions of Individual Proteins in the Membrane of Living Cells. In Anup K. Singh (Ed.) Single Cell Protein Analysis Methods in Molecular Biology. Springer
Anthony, Stephen M.↗
Monitoring Weld Pool Surface and Penetration Using Reversed Electrode Images
The three-dimensional weld pool top surface shape provides important information about the state of weld penetration during welding. In this study, a method was developed to quantitatively relate weld pool surface height to the reversed electrode image (REI) on the weld pool surface. This new feature was extracted from the weld pool image using a passive vision-based monitoring system during gas tungsten arc welding (GTAW). Due to the specular reflection of the weld pool top surface, the REI is visible on the weld pool surface during GTAW. The position of the REI was determined with a robust image processing algorithm. Based on the principle of light reflection, the distance between the electrode tip and the REI (DERI) was related to the weld pool surface height. By assuming the weld pool surface was a spherical mirror, a reflection model was established to calculate the surface height (SH) index based on the measurement of the DERI, arc length, and weld pool geometry. The proposed method was verified with bead-onplate welding experiments. The SH was positively related to the face reinforcement or depression of the weld bead. This method was applied to monitor the penetration state during beadonplate autogenous welding, particularly when a complete penetration weld was formed.
42 ENGINEERING↗
Crop Stem Width Estimation in Highly Cluttered Field Environment
We present two methods for estimation of crop stem width on small mobile robots. Stem width is an important phenotype needed by breeders and plant-biologists to measure plant growth, however, its manual measurement is cumbersome, in-accurate, and inefficient. The presented methods use a common image processing core that is designed to extract the foreground in the presence of significant leaf and stem clutter, view of other rows, and varying lighting, from a side-facing USB camera mounted on a small mobile robot. Using the extracted foreground, one approach uses estimates of robot velocity from wheel encoders and dense optical flow to estimate depth, while the other employs filtering of the LIDAR 2-D point cloud to estimate the depth. Both methods have been exhaustively validated against available hand-measurements on biomass sorghum (Sorghum bicolor) in real experimental fields. Experiments indicate that both methods are also applicable to other crops with cylindrical stems without significant modifications. The width estimation match on sorghum is 92.5% (using vision) and 98.2% (using vision and LIDAR) when compared against manual measurements by trained agronomists. Thus, our results clearly establish the feasibility of using small robots for stem-width estimation in realistic field settings. Furthermore, the techniques presented here can be utilized for automating other phenotypic measurements.
Choudhuri, Anwesa↗
Multi-aperture optical system for high-resolution imaging
Technologies pertaining to a multi-aperture optical imaging system are described herein. The multi-aperture optical system comprises an afocal optical telescope and a plurality of sub-apertures that receive light from the telescope. Each sub-aperture comprises a focusing optic that creates a full-field image onto an individual image sensor that outputs data indicative of an intensity of light received at the sensor. Image processing techniques can be used to combine the sensor outputs for spectral discrimination, to improve dynamic range or to enhance resolution of a scene.
47 OTHER INSTRUMENTATION↗
Digital Time-Resolved Spot Diagnostic
The digital time-resolved spot diagnostic (D-TRSD) is a fast-response, time-dependent beam diagnostic for measuring the emitted spot size and beam target interactions from radiographic x-ray sources. Legacy streaked TRSD analysis requires image processing and data extraction, which give noisy results. Processed data are used to extract edge spread and corresponding line spread functions. Digital TRSD simplifies this process, eliminating the complexity of streak camera–based systems.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
On the Efficacy and High-Performance Implementation of Quaternion Matrix Multiplication
Quaternion symmetry is ubiquitous in the physical sciences. As such, much work has been afforded over the years to the development of efficient schemes to exploit this symmetry using real and complex linear algebra. Recent years have also seen many advances in the formal theoretical development of explicitly quaternion linear algebra with promising applications in image processing and machine learning. Despite these advances, there do not currently exist optimized software implementations of quaternion linear algebra. The leverage of optimized linear algebra software is crucial in the achievement of high levels of performance on modern computing architectures, and thus provides a central tool in the development of high-performance scientific software. In this work, a case will be made for the efficacy of high-performance quaternion linear algebra software for appropriate problems. In this pursuit, an optimized software implementation of quaternion matrix multiplication will be presented and will be shown to outperform a vendor tuned implementation for the analogous complex matrix operation. The results of this work pave the path for further development of high-performance quaternion linear algebra software which will improve the performance of the next generation of applicable scientific applications.
97 MATHEMATICS AND COMPUTING↗
Robustness of deep learning algorithms in astronomy -- galaxy morphology studies
Deep learning models are being increasingly adopted in wide array of scientific domains, especially to handle high-dimensionality and volume of the scientific data. However, these models tend to be brittle due to their complexity and overparametrization, especially to the inadvertent adversarial perturbations that can appear due to common image processing such as compression or blurring that are often seen with real scientific data. It is crucial to understand this brittleness and develop models robust to these adversarial perturbations. To this end, we study the effect of observational noise from the exposure time, as well as the worst case scenario of a one-pixel attack as a proxy for compression or telescope errors on performance of ResNet18 trained to distinguish between galaxies of different morphologies in LSST mock data. We also explore how domain adaptation techniques can help improve model robustness in case of this type of naturally occurring attacks and help scientists build more trustworthy and stable models.
79 ASTRONOMY AND ASTROPHYSICS↗
Development and Validation of Resistance-Capacitance Model (RCM) for Phase Change Material (PCM) Embedded in 3D Periodic Structures
The low thermal conductivity of Phase Change Materials (PCM) can be improved with extended surfaces such as additively manufactured 3D periodic lattice structures. Three different aluminum alloy-based lattices (base sizes 10, 20, and 40 mm) with average porosity of 0.95 filled with paraffin wax, with a nominal phase change temperature of 55°C, were experimentally investigated. In this work, a computationally efficient 2D Resistance Capacitance-based model (RCM) was developed for predicting the thermal characteristics of these geometries. Non-uniform porosity in the PCM-metal domain was estimated using image processing and served as model input. The solver does not solve for higher-order physics as in CFD but still can provide a good prediction of thermal resistance and energy storage at a very low computational cost. The simulation-to-real-time factor for this geometry is of the order of 10-4, while CFD simulations typically have a real-time factor greater than 1. The model was validated against the experimental data for melting under three different heat fluxes (6250 W/m2, 12500 W/m2, and 18750 W/m2). The mean deviation of the predicted average PCM temperature was between 1.34 K-2.81 K for different cases. The maximum average temperature deviation of 5.45 K was observed for the 20 mm geometry at the highest heat flux test condition. The effects of natural convection were neglected in the model, but the predicted PCM temperature and energy storage still showed good agreement with the experimental data.
25 ENERGY STORAGE↗
WavPool: A New Block for Deep Neural Networks
Modern deep neural networks comprise many operational layers, such as dense or convolutional layers, which are often collected into blocks. In this work, we introduce a new, wavelet-transform-based network architecture that we call the multi-resolution perceptron: by adding a pooling layer, we create a new network block, the WavPool. The first step of the multi-resolution perceptron is transforming the data into its multi-resolution decomposition form by convolving the input data with filters of fixed coefficients but increasing size. Following image processing techniques, we are able to make scale and spatial information simultaneously accessible to the network without increasing the size of the data vector. WavPool outperforms a similar multilayer perceptron while using fewer parameters, and outperforms a comparable convolutional neural network by ~ 10% on relative accuracy on CIFAR-10.
McDermott, Samuel D.↗
Power System Waveform Datasets for Machine Learning
The desire for increased visibility across the electricity grid will necessarily increase the deployment of sensing and measurement devices and associated data management needs to unprecedented levels. For the existing sensing and measurement infrastructure, there remains a great amount of “value” yet to be extracted through advanced data management and analytics. Availability of more data will not, by itself, lead to changes in grid visibility, security, and resiliency. To create the predictive and prescriptive environment required to enable new markets and transactions for customer revenue and a reliable grid, the data must be collected, organized, evaluated, and analyzed using sophisticated algorithms to provide actionable information allowing operators and customers to reliably manage an increasingly complex grid. Progress in artificial intelligence (AI) has been largely driven by large, publicly available datasets that can be used to train AI algorithms such as MNIST, a database of handwritten images of digits, and ImageNet, an image database of everyday objects. These types of publicly available databases of real-world training datasets have been largely credited for advancement of image processing, computer vision, and deep learning algorithms that these use cases deploy. However, in the power systems industry to date, there are few databases with proper event labeling, and data access to a publicly available collection of power system event waveforms that will allow users to interact with grid signature data. Publicly available datasets of power system event waveforms, such as the DOE/EPRI dataset, often lack critical metadata or contain limited examples of each event type, and data formats vary widely across these datasets.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Deep learning methods for event verification and image re-purposing detection
Systems and methods herein describe accessing an image, generating a resized image, generating an image feature vector by applying an image classification neural network to the resized image, generating analysis of the image by processing the image feature vector using a machine-learning classifier trained to analyze the image feature vector, and based on the analysis, determining an event that is attributed to the image.
Nataraj, Lakshmanan↗
Dimensional Analysis of Russian 'Status-6' Nuclear Torpedo - 18097
Recently photographs of documents containing schematics of the Russian Status-6 Nuclear Torpedo were leaked. There have been no public attempts to assess the danger this weapon presents. This is due to the fact the leaked schematics are of low image quality and many of the written notes within the document are illegible. In this paper we attempt to extract as much information from the single leaked schematic as possible. By using dimensional analysis, 3D modeling, and previously available torpedo information we have attempted to assess the Status-6 Nuclear Torpedo. We expect this approach to provide the most extensive publicly available review of the torpedo as possible. (authors)
97 MATHEMATICS AND COMPUTING↗
Application of the TITAN Hybrid Deterministic Transport Code to Medical Physics - Paper 118
The TITAN code is a 3-dimensional parallel deterministic transport code that solves the linear Boltzmann equation. The TITAN code has hybrid algorithms that are ideal for medical physics applications: i) SN and characteristics method and ii) SN with a fictitious quadrature set and ray-tracing. These hybrid algorithms have been used in the simulation of computed tomography (CT) and single photon emission computed tomography (SPECT) In this paper, the hybrid algorithms developed for medical physics applications in the TITAN code are discussed. First, the TITAN code's block-oriented approach for utilizing SN and characteristic solvers for CT is presented and results for a simple test case are given. Second, the paper describes a hybrid formulation for SPECT with an SN solver in the phantom, a fictitious quadrature set for directions from the phantom surface passing through the collimator, and a simplified ray-tracing algorithm for particle transport to the detector. Finally, ongoing work is described and concluding remarks are given. The TITAN code's hybrid algorithms allow it to efficiently solve problems with low-scatter regions, which are frequently found in medical physics applications. The TITAN code has been used to solve a simplified CT problem and generate projection images for SPECT simulations. The WCOS technique was developed to improve the TITAN code's collimator representation. For these applications, the TITAN code was shown to require significantly less computation time than MCNP5. Work is currently underway to implement an iterative image reconstruction algorithm in the TITAN code. A script that calls the TITAN code to complete the forward projection step in SPECT has already been used to reconstruction a 2-dimensional phantom. The ability of TITAN to fully model particle transport in the patient in short computation times is expected to be a great advantage to iterative reconstruction. (authors)
61 RADIATION PROTECTION AND DOSIMETRY↗
Identifying Neutrino Final States and Energies in MicroBooNE with New Deep-Learning Based LArTPC Reconstruction Frameworks
MicroBooNE, a Liquid Argon Time Projection Chamber (LArTPC) located in the $\nu_{\mu}$-dominated Booster Neutrino Beam at Fermilab, has been studying $\nu_{e}$ charged-current (CC) interaction rates to shed light on the MiniBooNE low energy excess. The LArTPC technology employed by MicroBooNE provides the capability to image neutrino interactions with mm-scale precision. Computer vision and other machine learning techniques are promising tools for image processing that could boost efficiencies for selecting $\nu_{e}$-CC and other rare signals, reduce cosmic and beam-induced backgrounds, and improve the reconstruction of neutrino energies. The MicroBooNE experiment has been at the forefront of developing and testing such techniques for use in physics analyses. In this poster we overview deep-learning based reconstruction methods. We will showcase the use of a recurrent neural network to estimate neutrino energies and present a new reconstruction framework that uses convolutional neural networks to locate neutrino interaction vertices, tag pixels with track and shower labels, and perform particle identification on reconstructed clusters. We will present studies characterizing the performance of these new tools and demonstrate their effectiveness through their use in an inclusive $\nu_{e}$-CC event selection.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
ML-driven image reconstruction can speed up microscopy data processing by 500x with image quality improvement up to 70%.
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Dingreville, Remi Philippe Michel↗