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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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DOE OSTI2016

Fibrothorax as a Sequlae of Induced Pneumothoraces

A 91 years old female presented with chest pain and shortness of breath after a fall. Chest radiograph showed a very large left-sided fluid collection surrounded by calcified pleura and collapse of the left lung (Figure 1). CT scan of chest with contrast revealed a chronic appearing increased density left pleural effusion contained within thick calcified pleura (). Upon further questioning patient reported that when she was 20 years old she had repeated punctures to her left chest to collapse the lung for the treatment of suspected tuberculosis. The air-filled pleural space eventually got transformed into calcified pleura with contained effusion. No further intervention was performed for this effusion as the patient’s symptoms improved. She did not have any symptoms before the fall.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2023

Vehicle Lateral Offset Estimation Using Infrastructure Information for Reduced Compute Load

Accurate perception of the driving environment and a highly accurate position of the vehicle are paramount to safe Autonomous Vehicle (AV) operation. AVs gather data about the environment using various sensors. For a robust perception and localization system, incoming data from multiple sensors is usually fused together using advanced computational algorithms, which historically requires a high-compute load. To reduce AV compute load and its negative effects on vehicle energy efficiency, we propose a new infrastructure information source (IIS) to provide environmental data to the AV. The new energy–efficient IIS, chip–enabled raised pavement markers are mounted along road lane lines and are able to communicate a unique identifier and their global navigation satellite system position to the AV. This new IIS is incorporated into an energy efficient sensor fusion strategy that combines its information with that from traditional sensor. IIS reduce the need for camera imaging, image processing, and LIDAR use and point cloud processing. We show that IIS, when combined with traditional sensors, results in more accurate perception and localization outcomes and a reduced AV compute load.

Sharma, Sachin↗

DOE OSTI2020

Computing water flow through complex landscapes – Part 2: Finding hierarchies in depressions and morphological segmentations

Depressions – inwardly draining regions of digital elevation models – present difficulties for terrain analysis and hydrological modeling. Analogous “depressions” also arise in image processing and morphological segmentation, where they may represent noise, features of interest, or both. Here we provide a new data structure – the depression hierarchy – that captures the full topologic and topographic complexity of depressions in a region. We treat depressions as networks in a way that is analogous to surface-water flow paths, in which individual sub-depressions merge together to form meta-depressions in a process that continues until they begin to drain externally. This hierarchy can be used to selectively fill or breach depressions or to accelerate dynamic models of hydrological flow. Complete, well-commented, open-source code and correctness tests are available on GitHub and Zenodo.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

LSST Commissioning Camera

The LSST Commissioning Camera (LSSTComCam) was mounted on the Simonyi Survey Telescope in August 2022 and observed for seven weeks in late 2024 until it was removed from the telescope in December 2024. It is a smaller, fully functional version of LSSTCam, with only the central raft of 9 4k x 4k CCDs (all ITL sensors) and comprising 144 megapixels. Every CCD has 16 amplifiers, each reading 1 million pixels. LSSTComCam was designed to enable end-to-end testing of the observatory’s systems, including data acquisition, image processing, and observatory operations.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

DMTN-277: The Monster: A reference catalog with synthetic ugrizy-band fluxes for the Vera C. Rubin observatory

In order to facilitate bootstrap photometric calibrations of early Rubin Observatory data we have created an all sky reference catalog called The Monster. This reference catalog uses a rank-ordered set of other reference catalogs to generate synthetic ugrizy-band fluxes that can be used calibrate images processed with the LSST science pipelines. This document describes the methodology used to create The Monster, documents the input external reference catalogs, and performs basic data validation of the first version of The Monster.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2016

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↗

DOE OSTI2015

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.↗

DOE OSTI2017

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↗

DOE OSTI2018

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↗

DOE OSTI2018

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↗

DOE OSTI2019

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 ↗

DOE OSTI2019

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↗

DOE OSTI2021

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↗

DOE OSTI2022

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↗

DOE OSTI2023

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.↗

DOE OSTI2024

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↗

DOE OSTI2023

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↗

DOE OSTI2018

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↗