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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Real-time biomass feedstock particle quality detection using image analysis and machine vision

Abstract A common and costly challenge in the nascent biorefinery industry is the consistent handling and conveyance of biomass feedstock materials, which can vary widely in their chemical, physical, and mechanical properties. Solutions to cope with varying feedstock qualities will be required, including advanced process controls to adjust equipment and reject feedstocks that do not meet a quality standard. In this work, we present and evaluate methods to autonomously assess corn stover feedstock quality in real time and provide data to process controls with low-cost camera hardware. We explore the use of neural networks to classify feedstocks based on actual processing behavior and pixel matrix feature parameterization to further assess particle attributes that may explain the variable processing behavior. We used the pretrained ResNet neural network coupled with a gated recurrent unit (GRU) time-series classifier trained on our image data, resulting in binary classification of feedstock anomalies with favorable performance. The textural aspects of the image data were statistically analyzed to determine if the textural features were predictive of operational disruptions. The significant textural features were angular second moment, prominence, mean height of surface profile, mean resultant vector, shade, skewness, variation of the polar facet orientation, and direction of azimuthal facets. Expansion of these models is recommended across a wider variety of labeled feedstock images of different qualities and species to develop a more robust tool that may be deployed using low-cost cameras within biorefineries.

09 BIOMASS FUELS↗
DOE OSTI2025

Location generalizability of image-based air quality models

This paper is to be submitted at the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Computer Vision for Earth Observation workshop. The full paper abstract is below: The ability to rapidly quantify atmospheric pollutants is important both for global emissions monitoring and for mitigating the adverse effects that follow a hazardous chemical release. In the aftermath of a chemical release, imagery is often the only available resource to assess local conditions. Recent work has demonstrated initial success in predicting particulate matter pollution from imagery; however, these results are tied to a specific site and do not generalize to new geographic locations. In this work, we seek to understand how easily deep learning models generalize to new locations in the context of image-based air quality assessments, targeting two distinct tasks: (1) broad measures of particulate matter pollution, and (2) the mass of a given chemical released in hazardous plumes. For the latter, we focus on sulfur dioxide, a toxic aerosol and a major component of particulate matter pollution caused by industrial fossil fuel consumption. To develop a model that operates in the widest possible range of environments, we test different training strategies, including the use of new geolocation foundation models. The best performing models achieve >80% accuracy when evaluating unseen imagery at previously seen sites, but we find significant drops in performance when evaluating imagery from unseen sites, at best 65%. Additionally, we present the public release of the National Parks Air Quality Index Dataset, a new medium-sized dataset that pairs imagery with sensor-based air quality measurements at 15 different national parks.

Byler, Eleanor B. [BATTELLE (PACIFIC NW LAB)]↗
DOE OSTI2024

Uncertainty quantification and propagation in lithium-ion battery electrodes using bayesian convolutional neural networks

The complex nature of manufacturing processes stipulates electrodes to possess high variability with increased heterogeneity during production. X-ray computed tomography imaging has proved to be critical in visualizing the complicated stochastic particle distribution of as-manufactured electrodes in lithium-ion batteries. However, accurate prediction of their electrochemical performance necessitates precise evaluation of kinetic and transport properties from real electrodes. Image segmentation that characterizes voxels to particle/pore phase is often meticulous and fraught with subjectivity owing to a myriad of unconstrained choices and filter algorithms. Here we utilize a Bayesian convolutional neural network to tackle segmentation subjectivity and quantify its pertinent uncertainties. Otsu inter-variance and Blind/Referenceless Imaging Spatial Quality Evaluator are used to assess the relative image quality of grayscale tomograms, thus evaluating the uncertainty in the derived microstructural attributes. We analyze how image uncertainty is correlated with the uncertainties and magnitude of kinetic and transport properties of an electrode, further identifying pathways of uncertainty propagation within microstructural attributes. The coupled effect of spatial heterogeneity and microstructural anisotropy on the uncertainty quantification of transport parameters is also understood. This work demonstrates a novel methodology to extract microstructural descriptors from real electrode images through quantification of associated uncertainties and discerning the relative strength of their propagation, thus facilitating feedback to manufacturing processes from accurate image based electrochemical simulations.

25 ENERGY STORAGE↗
DOE OSTI2020

Effect of defocusing on the quality of quantum ghost images

The effect of defocusing due to the finite thickness of the crystal, in which parametric scattering occurs, on the spatial resolution of ghost images is considered. The necessary relationships are presented, and methods of levelling this distorting factor and improving the quality of ghost images are proposed. (paper)

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Evaluation Challenges for the Application of Extended Reality Devices in Medicine

Augmented and virtual reality devices are being actively investigated and implemented for a wide range of medical uses. However, significant gaps in the evaluation of these medical devices and applications hinder their regulatory evaluation. Addressing these gaps is critical to demonstrating the devices’ safety and effectiveness. We outline the key technical and clinical evaluation challenges discussed during the US Food and Drug Administration’s public workshop, “Medical Extended Reality: Toward Best Evaluation Practices for Virtual and Augmented Reality in Medicine” and future directions for evaluation method development. Evaluation challenges were categorized into several key technical and clinical areas. Finally, we highlight current efforts in the standards communities and illustrate connections between the evaluation challenges and the intended uses of the medical extended reality (MXR) devices. Participants concluded that additional research is needed to assess the safety and effectiveness of MXR devices across the use cases.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Sensitivity of back‐projection algorithm (BPA) synthetic aperture radar (SAR) image formation to initial position, velocity, and attitude navigation errors

Abstract The Back‐Projection Algorithm (BPA) is a time‐domain‐matched filtering technique to form synthetic aperture radar (SAR) images. To produce high‐quality BPA images, precise navigation data for the radar platform must be known. Errors in position, velocity, or attitude result in improperly formed images that are corrupted by shifting and blurring. The contribution of this paper is the development of analytical expressions that characterise the relationship between navigation errors and image formation errors from an inertial navigation point of view, where trajectory estimation errors in position, velocity, and attitude propagate through time and cause compounding errors in the vehicle state vector. These analytical expressions are verified via simulated image formation and real‐data image formation.

Lindstrom, Colton↗
DOE OSTI2022

Exploiting voxel-sparsity for bone imaging with sparse-view cone-beam computed tomography

An optimization-based image reconstruction frame work is developed specifically for bone imaging. This framework exploits voxel-sparsity by use of ℓ 1 -norm image regularization and it enables image reconstruction from sparse-view cone-beam computed tomography (CBCT) acquisition. The effectiveness of the voxel-sparsity regularization is enhanced by using a blurred image representation. Ramp-filtering is included in the data discrepancy term and it has the effect of acting as a preconditioner, reducing the necessary number of iterations. The bone image reconstruction framework is demonstrated on CBCT data taken from an equine metacarpal condyle specimen.

Bone imaging↗
DOE OSTI2022

Image Analysis for Rapid Assessment and Quality-Based Sorting of Corn Stover

Imaging in the visible spectrum is a low-cost tool that can be readily deployed for in-field or over-belt monitoring of biomass quality for bio-refining operations. Rapid image analysis coupled with innovative preprocessing may reduce the impacts of feedstock variability through identification of contaminants or other material attributes to guide selective sorting and quality management. Image analysis was employed to evaluate the quality of corn stover in red-green-blue (RGB) chromatic space. This study used controlled, bench-scale imaging as a proof-of-concept for rapid quality assessment of corn stover based on variations in material attributes, including chemical and physical attributes, that relate to biological degradation and soil contamination. Additionally, logistic regression-based classification algorithms were used to develop a method for biomass screening as a function of biological degradation or soil contamination. This study demonstrated the use of image analysis to extract features from RGB color space to investigate variations in critical material attributes from chemical composition of corn stover. Fourier transform infrared (FT-IR) suggested a correlation between red band intensity and biological degradation, while detailed surface texture analysis was found to distinguish among variations in ash. These insights offer promise for development of a rapid screening tool that could be deployed by farmers for in-field assessment of biomass quality or biorefinery operators for in-line sorting and process optimization.

09 BIOMASS FUELS↗
DOE OSTI2023

Coarse-to-fine Task-driven Inpainting for Geoscience Images

The processing and recognition of geoscience images have wide applications. Most of existing researches focus on understanding the high-quality geoscience images by assuming that all the images are clear. However, in many real-world cases, the geoscience images might contain occlusions during the image acquisition. This problem actually implies the image inpainting problem in computer vision and multimedia. As far as we know, all the existing image inpainting algorithms learn to repair the occluded regions for a better visualization quality, they are excellent for natural images but not good enough for geoscience images, and they never consider the following geoscience task when developing inpainting methods. Here, this paper aims to repair the occluded regions for a better geoscience task performance and advanced visualization quality simultaneously, without changing the current deployed deep learning based geoscience models. Because of the complex context of geoscience images, we propose a coarse-to-fine encoder-decoder network with the help of designed coarse-to-fine adversarial context discriminators to reconstruct the occluded image regions. Due to the limited data of geoscience images, we propose a MaskMix based data augmentation method, which augments inpainting masks instead of augmenting original images, to exploit the limited geoscience image data. The experimental results on three public geoscience datasets for remote sensing scene recognition, cross-view geolocation and semantic segmentation tasks respectively show the effectiveness and accuracy of the proposed method. The code is available at: https://github.com/HMS97/Task-driven-Inpainting.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

In-Line Optical Transmission Imaging of Decals for Quality Control - Task 3

Quality monitoring is a critical aspect for manufacturing systems. Ideally the monitoring would be done in-line, be non-contact, non-destructive, and fast. This would enable reduced scrap and higher throughput. This poster presents an optical transmission method for evaluating and mapping coatings. With the method shown in the poster we can visualize optical variations on the macro and micro scales. This allows us to see the overall trend in loading in both the cross web and down web directions. Furthermore, we can visualize defects such dewetting spots, streaks, clumps, and pinholes where there is a lack of coating. The optical transmission signal has been found to be proportional to the IrOx loading signal using XRF measurements. Therefore, an optical transmission setup can be installed in-line and allow for a fast, non-contact method for mapping loading variations and defects.

coating uniformity↗
DOE OSTI2023

BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. Furthermore, we observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Limiting the number of quantisation levels of a harmonic lens as a method for improving the quality of the generated image

The characteristics (modulation transfer functions) of harmonic lenses made by direct laser patterning of resist are experimentally studied. The influence of technological errors in the manufacture of lenses on the formation of the point spread function is modelled for harmonic lenses with 32 and 256 quantisation levels. Direct laser writing is used to produce lenses with these numbers of levels. It is experimentally shown that the modulation transfer function of the harmonic lens with 32 quantisation levels is significantly greater at high spatial frequencies (50 mm{sup −1}). (paper)

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Picometer-Precision Atomic Position Tracking through Electron Microscopy

The modern aberration-corrected scanning electron microscopes have successfully achieved direct visualization of atomic columns with sub-angstrom resolution. With this significant progress, advanced image quantification and analysis is still at its early stages. In this work, we present the complete pathway for the metrology of atomic resolution STEM images. This includes: 1) tips for acquiring high-quality STEM images; 2) denoising and drift-correction for enhancing measurement accuracy; 3) obtaining initial atom positions; 4) indexing the atoms based on unit cell vectors; 4) quantifying the atom column positions with either 2D-gaussian single peak fitting or 5) multi-peak fitting routines for slightly overlapping atomic columns; 6) quantification of lattice distortion/strain within the crystal structures or at the defects/interfaces where the lattice periodicity is disrupted, and 7) some common methods to visualize and present the analysis. Furthermore, a simple self-developed free MATLAB app (EASY-STEM) with a graphical user interface (GUI) will be introduced that can help with the analysis of STEM images without the need for writing dedicated analysis code or software. The advanced data analysis methods presented here can be applied for the local quantification of defect relaxations, local structural distortions, local phase transformations, and non-centrosymmetry in a wide range of materials.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

High signal-to-noise ratio differential conductance spectroscopy

The scanning tunneling microscope (STM) has enabled manipulation and interrogation of surfaces with atomic-scale resolution. Electronic information about a surface is obtained by combining the imaging capability of the STM with scanning tunneling spectroscopy, i.e., measurement of current-voltage (I/V) characteristics of the surface. We propose a change in the STM feedback loop that enables capturing a higher quality dI/dV image. A high frequency dither voltage is added to the bias voltage of the sample, and the fundamental frequency component of the resulting current is demodulated. The in-phase component of this signal is then plotted along with the X and Y position data, constructing the dI/dV image. Here, we show that by incorporating notch filters in the STM feedback loop, we may utilize a high-amplitude dither voltage to significantly improve the quality of the obtained dI/dV image.

42 ENGINEERING↗
DOE OSTI2024

Using AI for Wave-front Estimation with the Rubin Observatory Active Optics System

Abstract The Vera C. Rubin Observatory will, over a period of 10 yr, repeatedly survey the southern sky. To ensure that images generated by Rubin meet the quality requirements for precision science, the observatory will use an active-optics system (AOS) to correct for alignment and mirror surface perturbations introduced by gravity and temperature gradients in the optical system. To accomplish this, Rubin will use out-of-focus images from sensors located at the edge of the focal plane to learn and correct for perturbations to the wave front. We have designed and integrated a deep-learning (DL) model for wave-front estimation into the AOS pipeline. In this paper, we compare the performance of this DL approach to Rubin’s baseline algorithm when applied to images from two different simulations of the Rubin optical system. We show the DL approach is faster and more accurate, achieving the atmospheric error floor both for high-quality images and low-quality images with heavy blending and vignetting. Compared to the baseline algorithm, the DL model is 40× faster, the median error 2× better under ideal conditions, 5× better in the presence of vignetting by the Rubin camera, and 14× better in the presence of blending in crowded fields. In addition, the DL model surpasses the required optical quality in simulations of the AOS closed loop. This system promises to increase the survey area useful for precision science by up to 8%. We discuss how this system might be deployed when commissioning and operating Rubin.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

A combination interferometric and morphological image processing approach to rapid quality assessment of additively manufactured cellular truss core components

Advanced manufacturing (AM) processes such as laser powder bed fusion (LPBF) are increasingly capable of fabricating components with useful and unprecedented mechanical properties by incorporating complex internal bracing structures. From the standpoint of quality control and assessment, however, internally complex assemblies present significant build-verification challenges. Here we propose a hybrid approach to the inspection involving the application of computer-aided speckle interferometry (CASI) and morphological image processing as a rapid, inexpensive, and facile method for AM quality control. The described methodology has low capital equipment costs, is full-field and non-contact, can be used in an industrial setting, and has very low requirements in terms of operator training and expertise. Consisting primarily of the combination of image processing software with a simple optical system of variable sensitivity, the method is shown to be effective for inspection of a titanium honeycomb component subjected to differential pressure. Results are compared to those achieved with computed tomography (CT), immersion ultrasound testing (UT), and optical holographic interferometry. Here we propose several possible processing strategies for automated quality assessment based on this powerful hybrid approach.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Image processing pipeline for AI-driven nanoparticle megalibrary characterization

Recent innovations have made it possible to produce megalibraries, millions of structurally and compositionally distinct nanoparticles on a chip. These megalibraries yield vast volumes of data that are impossible to analyze manually, necessitating the development of automated tools. In previous work, we created a binary classification machine learning model to select quality nanoparticle images for downstream analysis. In this work, we show that adding a custom image processing step before training can produce significantly higher-performing models in a fraction of the time and make them more robust to different image noise levels and microscope acquisition settings. The image processing pipeline proposed here effectively cleans raw nanoparticle images, enhances key features, and allows us to use much lower resolution images and simpler neural network model architectures. These features result in higher performance and significant cost savings. Experiments demonstrate superior performance relative to baseline, including an 18.2% improvement in recall and a 13.1% increase in accuracy. Given the high cost of downstream analysis, it is critical to minimize false positives, and our best-performing model reaches a precision of 95.9% and a weighted F-score of 95.1% on an unseen test set. Additionally, model training time is reduced from hours to less than a minute. We also show that, using this custom image processing pipeline, model performance is significantly improved at lower pixel resolutions compared to downsizing alone. We expect that adopting this pipeline for AI-driven automated nanoparticle characterization will allow researchers to rapidly and accurately analyze much greater volumes of data, thereby accelerating materials discovery.

77 NANOSCIENCE AND NANOTECHNOLOGY↗