Neutron Imaging at LANSCE: Characterizing Materials for the Next Generation of Nuclear Reactor Designs
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Long, Alexander Makenzie↗
SEARCH · Engineering Papers
Results for “image generation”
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Deep Generative Models in Energy System Applications: Review, Challenges, and Future Directions
In recent years, with the advent of mature machine learning products like ChatGPT, Stable Diffusion, and Sora, the world has witnessed tremendous changes driven by the rapid development of generative artificial intelligence (GAI). Beyond applications in text, speech, image, and video creation, deep generative models (DGMs) underpinning these cutting-edge technologies have also been employed by domain researchers to address scientific and engineering challenges. This paper aims to fill a gap in the research community by providing a systematic review of how DGMs have been utilized in energy system applications. After introducing four most popular DGMs, we review and categorize 196 research articles into five focus areas: data generation, forecasting, situational awareness, modeling, and optimal decision-making. Through this classification, we uncover trends in how DGMs are employed for each type of problem, highlighting GAI techniques that contribute to breakthroughs over traditional methods. We discuss limitations in existing literature, engineering challenges, and propose future directions, all tailored to the unique nature of problems in energy system engineering. Our goal is to offer insights for energy system domain researchers, providing a comprehensive view of existing studies and potential future opportunities.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Automated Scoring of Morphological Changes in Images of Pentaerythritol Tetranitrate
Recent advances in characterization techniques that generate large datasets of material microstructure images require robust, automated image-processing. We applied an unsupervised anomaly detection method called feature anomaly detection system (FADS) to automatically detect and quantify microstructure changes in images of the explosive pentaerythritol tetranitrate (PETN) aged at various temperatures. We demonstrated the FADS approach on two-dimensional images extracted from computed tomography scans, but the same technique can be readily applied to other imaging modalities. FADS calculates anomaly scores on the basis of differences in filter activations of nominal and test data in pretrained convolutional neural networks. The FADS scores successfully differentiated between pristine PETN and PETN aged at a temperature where material coarsening occurred. Morphological metric analysis of segmented images verified observed trends in FADS scores as a function of aging temperature and aging time, specifically by calculating volume fractions, specific boundary lengths, two-point correlation functions, and local thicknesses. Here, the FADS technique has two important advantages compared to traditional morphological analysis: First, it uses grayscale images as input, rather than images that are segmented to separate the appropriate phases; and second, FADS scores capture any type of changes among image sets, rather than requiring prior knowledge or selection of a relevant set of metrics.
Accelerated aging↗
Multimodal imaging and machine learning to enhance microscope images of shale
A machine learning based image processing workflow is presented to enhance shale source rock microscopic images obtained using diverse imaging platforms. Images were acquired from a 30 μm diameter cylindrical Vaca Muerta shale sample using both nondestructive Transmission X-Ray Microscopy (TXM, alternately referred to as nano computed tomography) and destructive Focused Ion Beam-Scanning Electron Microscopy (FIB-SEM). Output cross-sectional images from each modality were aligned using a combination of manual and automated registration techniques to create a registered image dataset. We then apply this dataset for two image processing tasks: prediction of image cross sections with SEM-like resolution from nondestructive TXM data and repair of charged region artifacts (localized accumulation of electrons) within SEM images. The image processing algorithms for both tasks use deep learning models, specifically image-to-image Convolutional Neural Networks (CNNs) and conditional Generative Adversarial Networks (cGANs). In the image enhancement tasks, we are able to achieve significant qualitative and quantitative improvement in TXM images. Here, the best model reaches an average Peak Signal to Noise Ratio (PSNR) of 15.8 dB. Conditioning on TXM data is also shown to reduce artifacts from SEM charging, achieving an average PSNR of 25.8 dB. Furthermore, our results suggest that properly trained and validated networks are capable of significant enhancement of images obtained using nondestructive techniques, thereby improving interpretation of two- and three-dimensional images while preserving samples for future use.
58 GEOSCIENCES↗
CryoSegNet: accurate cryo-EM protein particle picking by integrating the foundational AI image segmentation model and attention-gated U-Net
Picking protein particles in cryo-electron microscopy (cryo-EM) micrographs is a crucial step in the cryo-EM-based structure determination. However, existing methods trained on a limited amount of cryo-EM data still cannot accurately pick protein particles from noisy cryo-EM images. The general foundational artificial intelligence–based image segmentation model such as Meta’s Segment Anything Model (SAM) cannot segment protein particles well because their training data do not include cryo-EM images. Here, we present a novel approach (CryoSegNet) of integrating an attention-gated U-shape network (U-Net) specially designed and trained for cryo-EM particle picking and the SAM. The U-Net is first trained on a large cryo-EM image dataset and then used to generate input from original cryo-EM images for SAM to make particle pickings. CryoSegNet shows both high precision and recall in segmenting protein particles from cryo-EM micrographs, irrespective of protein type, shape and size. On several independent datasets of various protein types, CryoSegNet outperforms two top machine learning particle pickers crYOLO and Topaz as well as SAM itself. The average resolution of density maps reconstructed from the particles picked by CryoSegNet is 3.33 Å, 7% better than 3.58 Å of Topaz and 14% better than 3.87 Å of crYOLO. It is publicly available at https://github.com/jianlin-cheng/CryoSegNet
59 BASIC BIOLOGICAL SCIENCES↗
Super-resolution and segmentation deep learning for breast cancer histopathology image analysis
Traditionally, a high-performance microscope with a large numerical aperture is required to acquire high-resolution images. However, the images’ size is typically tremendous. Therefore, they are not conveniently managed and transferred across a computer network or stored in a limited computer storage system. As a result, image compression is commonly used to reduce image size resulting in poor image resolution. Here, we demonstrate custom convolution neural networks (CNNs) for both super-resolution image enhancement from low-resolution images and characterization of both cells and nuclei from hematoxylin and eosin (H&E) stained breast cancer histopathological images by using a combination of generator and discriminator networks so-called super-resolution generative adversarial network-based on aggregated residual transformation (SRGAN-ResNeXt) to facilitate cancer diagnosis in low resource settings. The results provide high enhancement in image quality where the peak signal-to-noise ratio and structural similarity of our network results are over 30 dB and 0.93, respectively. The derived performance is superior to the results obtained from both the bicubic interpolation and the well-known SRGAN deep-learning methods. In addition, another custom CNN is used to perform image segmentation from the generated high-resolution breast cancer images derived with our model with an average Intersection over Union of 0.869 and an average dice similarity coefficient of 0.893 for the H&E image segmentation results. Finally, we propose the jointly trained SRGAN-ResNeXt and Inception U-net Models, which applied the weights from the individually trained SRGAN-ResNeXt and inception U-net models as the pre-trained weights for transfer learning. The jointly trained model’s results are progressively improved and promising. We anticipate these custom CNNs can help resolve the inaccessibility of advanced microscopes or whole slide imaging (WSI) systems to acquire high-resolution images from low-performance microscopes located in remote-constraint settings.
60 APPLIED LIFE SCIENCES↗
Physics and chemistry from parsimonious representations: image analysis via invariant variational autoencoders
Electron, optical, and scanning probe microscopy methods are generating ever increasing volume of image data containing information on atomic and mesoscale structures and functionalities. This necessitates the development of the machine learning methods for discovery of physical and chemical phenomena from the data, such as manifestations of symmetry breaking phenomena in electron and scanning tunneling microscopy images, or variability of the nanoparticles. Variational autoencoders (VAEs) are emerging as a powerful paradigm for the unsupervised data analysis, allowing to disentangle the factors of variability and discover optimal parsimonious representation. Here, we summarize recent developments in VAEs, covering the basic principles and intuition behind the VAEs. The invariant VAEs are introduced as an approach to accommodate scale and translation invariances present in imaging data and separate known factors of variations from the ones to be discovered. We further describe the opportunities enabled by the control over VAE architecture, including conditional, semi-supervised, and joint VAEs. Several case studies of VAE applications for toy models and experimental datasets in Scanning Transmission Electron Microscopy are discussed, emphasizing the deep connection between VAE and basic physical principles. Python codes and datasets discussed in this article are available at https://github.com/saimani5/VAE-tutorials and can be used by researchers as an application guide when applying these to their own datasets.
36 MATERIALS SCIENCE↗
UVCGAN: UNet Vision Transformer cycle-consistent GAN for unpaired image-to-image translation
Unpaired image-to-image translation has broad applications in art, design, and scientific simulations. One early breakthrough was CycleGAN that emphasizes one-to-one mappings between two unpaired image domains via generative-adversarial networks (GAN) coupled with the cycle-consistency constraint, while more recent works promote one-to-many mapping to boost diversity of the translated images. Motivated by scientific simulation and one-to-one needs, this work revisits the classic CycleGAN framework and boosts its performance to outperform more contemporary models without relaxing the cycle-consistency constraint. To achieve this, we equip the generator with a Vision Transformer (ViT) and employ necessary training and regularization techniques. Compared to previous best-performing models, our model performs better and retains a strong correlation between the original and translated image. An accompanying ablation study shows that both the gradient penalty and self-supervised pre-training are crucial to the improvement. To promote reproducibility and open science, the source code, hyperparameter configurations, and pre-trained model are available at https: //github.com/LS4GAN/uvcgan.
97 MATHEMATICS AND COMPUTING↗
Reflection Optical Imaging to Study Oxygen Evolution Reactions
Discovery of high-efficiency water-splitting catalysts is critical for clean hydrogen fuel generation. Quantitative and reliable chemical imaging methods can play a crucial role in providing critical catalyst efficiency information for materials development and screening. Here, we developed a reflection optical imaging method to map the performance of transition metal oxide catalysts for oxygen evolution reaction (OER). The generated high concentration of oxygen in the electrolyte will decrease the refractive index of the solutions near the electrode and change the reflectivity of optical images. We successfully imaged and measured the OER on cobalt oxide, and the localized current density and overpotential distribution maps have been extracted. The oxidation and reduction of cobalt oxide can be imaged with our reflection optical imaging method as well. These maps showed that the catalytic activities are quite different at different locations, and some regions showed much higher activities. This is due to the non-uniformity created by the catalyst synthesis process and demonstrates the importance of using the optical imaging method to map the localized catalytic efficiency. In addition, the NiFe alloy’s OER catalytic activities have been studied as well. The proposed reflection optical imaging method could find many applications in catalyst activity measurement and screening.
Electrochemistry↗
H420 Imager Calibration Procedure: Field of View Scan
Gamma-ray imagers with coded apertures have a finite field of view (FOV) within which an image of a source can be generated. A “FOV scan” is a facet of imager calibration procedure that involves collecting specialized data for a range of source locations filling the FOV in order to understand and correct for differences in imager performance. Systematic distortions reveal relative displacements between internal imager components, which can be accounted for in data analysis. The success of this scan requires a stable imager position during the full measurement duration (~hours). This document summarizes the hardware and software tools required to conduct a FOV calibration scan of an H3D H420 Coded Aperture Gamma-Ray Imager.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Portable Detector For Fast Neutron And X-Ray Digital Radiography Using Transverse Anderson Localization Scintillating Nanoguides
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73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
The Satellite Image Simulation Toolkit
The Satellite Image Simulation Toolkit (SatIST) is a python software package designed to generate diverse and realistic satellite imaging scenarios. It serves as a toolkit for simulating data that supports the development and testing of algorithms used in satellite detection, calibration, and characterization. SIST provides a suite of simulation tools that allow users to replicate various satellite observation conditions, including sidereal and target tracking. By enabling the creation of scenarios that mimic real-world satellite operations, SIST facilitates advancements in satellite image data processing and the study of satellite behavior under different observational parameters.
Perloff, AlexxS [Lawrence Livermore National Labor↗
Advances in machine-learning-based sampling motivated by lattice quantum chromodynamics
Sampling from known probability distributions is a ubiquitous task in computational science, underlying calculations in domains from linguistics to biology and physics. Generative machine-learning (ML) models have emerged as a promising tool in this space, building on the success of this approach in applications such as image, text, and audio generation. Often, however, generative tasks in scientific domains have unique structures and features—such as complex symmetries and the requirement of exactness guarantees—that present both challenges and opportunities for ML. This Perspective outlines the advances in ML-based sampling motivated by lattice quantum field theory, in particular for the theory of quantum chromodynamics. Enabling calculations of the structure and interactions of matter from our most fundamental understanding of particle physics, lattice quantum chromodynamics is one of the main consumers of open-science supercomputing worldwide. Here, the design of ML algorithms for this application faces profound challenges, including the necessity of scaling custom ML architectures to the largest supercomputers, but also promises immense benefits, and is spurring a wave of development in ML-based sampling more broadly. In lattice field theory, if this approach can realize its early promise it will be a transformative step towards first-principles physics calculations in particle, nuclear and condensed matter physics that are intractable with traditional approaches.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Material identification system
A method and apparatus for identifying a material in an object. An image of the object generated from energy passing through the object is obtained by a computer system. The computer system estimates attenuations for pixels in a sensor system from the image of the object to form estimated attenuations. The estimated attenuations represent a loss of the energy that occurs from the energy passing through the object. The computer system also identifies the material in the object using the estimated attenuations and known attenuation information for identifying the material in the object.
Jimenez, Jr., Edward Steven↗
The updated DESGW processing pipeline for the third LIGO/VIRGO observing run
The DESGW group seeks to identify electromagnetic counterpartsof gravitational wave events seen by the LIGO-VIRGO network, such as thoseexpected from binary neutron star mergers or neutron star- black hole mergers.DESGW was active throughout the first two LIGO observing seasons, followingup several binary black hole mergers and the first binary neutron star merger,GW170817. We describe the modifications to the observing strategy generationand image processing pipeline between the second (ending in August 2017)and third (beginning in April 2019) LIGO observing seasons. The modifica-tions include a more robust observing strategy generator, further parallelizationof the image reduction software and dierence imaging processing pipeline,data transfer streamlining, and a web page listing identified counterpart candi-dates that updates in real time. Taken together, the additional parallelizationsteps enable us to identify potential electromagnetic counterparts within fullycalibrated search images in less than one hour, compared to the 3-5 hours itwould typically take during the first two seasons. These performance improve-ments are critical to the entire EM followup community, as rapid identification(or rejection) of candidates enables detailed spectroscopic followup by multipleinstruments as soon as possible, leading to more information about the environ-ment immediately following such gravitational wave events.
Herner, Kenneth R.↗
Synergistic Alignment of Low Aspect‐Ratio π‐Conjugated Molecules Enables Exceptional UV–vis–NIR Polarization Detection
Abstract Polarization detection enhances signal contrast and is widely utilized in diverse advanced applications. An ongoing challenge is the development of high‐performance polarization‐sensitive photodetectors based on optically anisotropic organic semiconductors, particularly in the near‐infrared (NIR) region. While uniaxially aligned π‐conjugated polymers with high aspect ratios exhibit strong linear dichroism and have shown promise, their limited NIR performance and heavy reliance on polymer material now represent critical limitations. Here, a breakthrough is reported in achieving giant linear dichroism and exceptional polarization detection with low aspect‐ratios (AR) non‐fullerene small‐molecule (NFSM) acceptors, extending polarization sensitivity from the UV–vis to the NIR range. An impressive dichroic ratio of 27.1 at 605 nm and 12.0 at 780 nm is demonstrated. The maximum polarization photocurrent ratio is 11.2 at 780 nm under parallel versus perpendicular polarized light. This unprecedented performance originates from synergistic molecular alignment, wherein NFSMs significantly enhance the uniaxial orientation of both the polymer matrix and the NFSMs themselves during self‐assembly and thermal annealing. Besides, such a linear‐polarization‐sensitive photodetectors (LPS‐PDs) are showcased in generating degree‐of‐linear‐polarization imaging. The work establishes NFSMs as a viable material system for next‐generation of organic LPS‐PDs and provides fundamental insights into structural origins of polarization sensitivity in low AR organic semiconductors.
Xue, Yingying↗
Extinction Imaging Diagnostics for In Situ Quantification of Soot within Explosively Generated Fireballs
Fireballs produced from the detonation of high explosives often contain particulates primarily composed of various phases of carbon soot. The transport and concentration of these particulates is of interest for model validation and emission characterization. Here, this work proposes ultra-high-speed imaging techniques to observe a fireball's structure and optical depth. An extinction-based diagnostic applied at two wavelengths indicates that extinction scales inversely with wavelength, consistent with particles in the Rayleigh limit and dimensionless extinction coefficients which are independent of wavelength. Within current confidence bounds, the extinction-derived soot mass concentrations agree with expectations based upon literature reported soot yields. Results also identify areas of high uncertainty where additional work is recommended.
33 ADVANCED PROPULSION SYSTEMS↗
Systems and methods for single-axis tracking via sky imaging and machine leanring comprising a neural network to determine an angular position of a photovoltaic power system
A system and method is disclosed for solar tracking and controlling an angular position of a photovoltaic power system. The solar tracking system includes an imaging device for capturing images of the sky; a solar position data generating module; and a control system comprising a neural network. The neural network has multiple convolutional layers to generate a first output associated with the images, and a solar position data module. A first dense layer module receives the solar position data and generates a second output. A second dense layer module receives the first output and the second output and generates a concatenated data sequence. A processor is programmed to generate a multi-planar irradiance signal (MPIS) in response to the concatenated data sequence, and determine an angular position of the PV power system and adjust the angular position in response to an angle of maximum irradiance.
Stein, Joshua↗