Automated analysis of grain morphology in TEM images using convolutional neural network with CHAC algorithm
Not Available
Xu, Xinyuan (ORCID:0000000238726275)↗
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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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Automated thermal mapping techniques using chromatic image analysis
Thermal imaging techniques are introduced using a chromatic image analysis system and temperature sensitive coatings. These techniques are used for thermal mapping and surface heat transfer measurements on aerothermodynamic test models in hypersonic wind tunnels. Measurements are made on complex vehicle configurations in a timely manner and at minimal expense. The image analysis system uses separate wavelength filtered images to analyze surface spectral intensity data. The system was initially developed for quantitative surface temperature mapping using two-color thermographic phosphors but was found useful in interpreting phase change paint and liquid crystal data as well.
Buck, Gregory M.↗
Automated thermal mapping techniques using chromatic image analysis
Thermal imaging techniques are introduced using a chromatic image analysis system and temperature sensitive coatings. These techniques are used for thermal mapping and surface heat transfer measurements on aerothermodynamic test models in hypersonic wind tunnels. Measurements are made on complex vehicle configurations in a timely manner and at minimal expense. The image analysis system uses separate wavelength filtered images to analyze surface spectral intensity data. The system was initially developed for quantitative surface temperature mapping using two-color thermographic phosphors but was found useful in interpreting phase change paint and liquid crystal data as well.
Buck, Gregory M.↗
HyperKube: A Kubernetes Based System for the Automation of Processing and Analysis of Hyperspectral Data Obtained from Multiple Hyperspectral Imaging Systems
Hyperspectral imagery is an emerging field of technology that has enormous potential for remote and proximal sensing in numerous areas of research. The plant phenotyping community is applying this technology to advance the throughput and accuracy of plant phenotypes based on airborne and lab-based hyperspectral imaging technology. Here we report an automated processing and analysis pipeline for four different hyperspectral imaging platforms, discuss the data issues involved, and present a strategy for computing and data architecture to handle hyperspectral data.
47 OTHER INSTRUMENTATION↗
Pit depth analysis using automated software for three containers with complete imaging
The 3013 surveillance program is tasked with ensuring the long term safety of plutonium storage in 3013-compliant containers. One component of this task is to understand corrosion features found on the interior sidewall of the inner container closure weld region (ICCWR). To this end, three containers were selected to have laser confocal microscope (LCM) 20X imaging of the entire ICCWR. This report describes the analysis of the LCM pit depth data using the automated corrosion analysis software. The analysis included determining maximum pit depths and distributions of pit depths, volumes, and diameters for all pit features identified on the three containers. Results provided insight into possible corrosion mechanisms and were consistent with previous pit growth model predictions.
36 MATERIALS SCIENCE↗
Tracking Dendritic Growth in Hydrogen-Based Hematite Reduction via Computer Vision
The reduction of hematite to metallic iron using hydrogen (H2) as a reducing agent presents a promising pathway for decarbonizing steel production. In this study, we employ a combination of in situ confocal scanning laser microscopy (CSLM) and advanced computer vision techniques to quantitatively analyze dendritic growth of ferrite during H2-based reduction of iron oxide at high temperatures. A workflow integrating Watershed Image Segmentation (WIS) and Lucas-Kanade Optical Flow (LKOF) is developed to extract both global and local kinetic information from time-resolved micrograph sequences. H2 reduction experiments conducted at 1400 degrees C and 1500 degrees C demonstrate a clear correlation between temperature and reduction rate, as evidenced by accuracy of fitted Johnson-Mehl-Avrami-Kolmogorov (JMAK) parameters. Optical flow analysis further elucidates the anisotropic and branched nature of dendritic growth, providing spatially resolved velocity fields that correlate well with global transformation kinetics. The proposed methodology demonstrates strong agreement with experimental measurements and literature values, offering a robust framework for automated image-based analysis to study kinetics through microstructural evolution in the reduction of iron ore, and likely other reaction-diffusion phenomena.
08 HYDROGEN↗
Three-dimensional segmentation of luminal and adventitial borders in serial intravascular ultrasound images
Intravascular ultrasound (IVUS) provides exact anatomy of arteries, allowing accurate quantitative analysis. Automated segmentation of IVUS images is a prerequisite for routine quantitative analyses. We present a new three-dimensional (3D) segmentation technique, called active surface segmentation, which detects luminal and adventitial borders in IVUS pullback examinations of coronary arteries. The technique was validated against expert tracings by computing correlation coefficients (range 0.83-0.97) and William's index values (range 0.37-0.66). The technique was statistically accurate, robust to image artifacts, and capable of segmenting a large number of images rapidly. Active surface segmentation enabled geometrically accurate 3D reconstruction and visualization of coronary arteries and volumetric measurements.
NASA Discipline Cardiopulmonary↗
Mathematical morphology for automated analysis of remotely sensed objects in radar images
A symbiosis of pyramidal segmentation and morphological transmission is described. The pyramidal segmentation portion of the symbiosis has resulted in low (2.6 percent) misclassification error rate for a one-look simulation. Other simulations indicate lower error rates (1.8 percent for a four-look image). The morphological transformation portion has resulted in meaningful partitions with a minimal loss of fractal boundary information. An unpublished version of Thicken, suitable for watersheds transformations of fractal objects, is also presented. It is demonstrated that the proposed symbiosis works with SAR (synthetic aperture radar) images: in this case, a four-look Seasat image of sea ice. It is concluded that the symbiotic forms of both segmentation and morphological transformation seem well suited for unsupervised geophysical analysis.
Daida, Jason M.↗
Artificial intelligence for geologic mapping with imaging spectrometers
This project was a three year study at the Center for the Study of Earth from Space (CSES) within the Cooperative Institute for Research in Environmental Science (CIRES) at the University of Colorado, Boulder. The goal of this research was to develop an expert system to allow automated identification of geologic materials based on their spectral characteristics in imaging spectrometer data such as the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). This requirement was dictated by the volume of data produced by imaging spectrometers, which prohibits manual analysis. The research described is based on the development of automated techniques for analysis of imaging spectrometer data that emulate the analytical processes used by a human observer. The research tested the feasibility of such an approach, implemented an operational system, and tested the validity of the results for selected imaging spectrometer data sets.
Kruse, F. A.↗
Open Specy 1.0: Automated (Hyper)spectroscopy for Microplastics
Microplastic spectral analysis is one of the most time-consuming processes in studying microplastic pollution, often requiring days per sample. Researchers are transitioning to automated batch and hyperspectral image analysis techniques to enhance efficiency. Open Specy, initially aimed at manual single-spectrum analysis, has now integrated automated methods. This updated version, Open Specy 1.0, introduces several new features, including two algorithms for automated processing (smoothing and particle compression), an extensive library containing over 40,000 open-source Raman and FTIR spectra, and two machine learning classifiers (logistic regression and k medoids) developed from this library. Furthermore, it includes a revamped user interface, an R package, and a benchmark data set for testing future advancements in automated techniques. Researchers evaluated various configurations for hyperspectral smoothing, particle identification, compression, and splitting, to achieve combined recovery rates between 50 and 150% particle counts, identities, and sizes with a coefficient of variation (CV) of less than 40% (the accredited standard). Mean absorbance times the standard deviation provided a consistent particle identification. Hyperspectral smoothing led to a 96% combined recovery rate and reduced variability (CV = 38%) compared to the 86% recovery (CV = 83%) of nonsmoothed controls. Additionally, compressing spectra for particles was significantly faster (>3x) and showed similar accuracy but with reduced variability than processing each pixel individually. Key challenges persist in automating spectral analysis, particularly in refining particle splitting algorithms, and improving identification routines to minimize false positives and negatives. In conclusion, new methods in sample preparation for better stabilization and dispersion of particles could overcome some of these issues.
13 HYDRO ENERGY↗
Automated Programmable Logic Controller Memory Forensics Using RGB Image Analysis and Deep Learning
The introduction of Industry 4.0 and Internet-based technologies has enhanced industrial control system operations but have inadvertently increased their vulnerabilities to cyber attacks. When an industrial control system is compromised, security analysts need to identify the root cause quickly to start the recovery process and develop mitigation strategies. Memory forensics is critical in the incident analysis process to ascertain what occurred. Approaches for analyzing the persistent memory in industrial control devices are limited and almost nonexistent for volatile memory. This chapter proposes an automated methodology for programmable logic controller memory dump analysis using computer vision and deep learning techniques. The methodology converts the sequences of bytes in a programmable logic controller memory dump to red-green-blue pixels and employs a deep learning model that learns the underlying patterns and features of pre-labeled forensic artifacts in images and segments them into distinct regions. The trained model is employed to automatically segment new memory images and identify forensic artifacts. Evaluation of the methodology on a Schneider Electric Modicon M221 programmable logic controller under code injection and code modification attacks demonstrates its ability to detect attack artifacts in memory dumps.
Asmar Awad, Rima [ORNL] (ORCID:0000000233407742)↗
Fuel spray measurements with an imaging-type spray analyzer
The application of an imaging-type, automated spray analysis system to measurements of fuel sprays requires consideration of a number of significant factors both in its use and in the interpretation of the data. These factors are reviewed and a procedure developed for one such instrument is described. The procedure involves optical scans along the optic axis of the system and an appropriate mathematical inversion scheme for interpretation of the results. The system is nominally capable of yielding the local size distribution function and the total number density at points along the optic axis for droplets from about 10 to 500 microns. No assumptions regarding the form of the size distribution function or the symmetry of the spray are required.
Skifstad, J. G.↗
Real-time laser holographic Interferometry for aerodynamics
Recent developments in thermoplastic recording holograms and advancements in automated image digitalization and analysis make real-time laser holographic interferometry feasible for two-dimensional flows such as airfoil flows. Typical airfoil measurements would include airfoil presssure distributions, wake and boundary layer profiles, and flow field density contours. This paper addresses some of the problems and requirements of a real-time laser holographic interferometer.
Lee, George↗
Real-time laser holographic interferometry for aerodynamics
Recent developments in thermoplastic recording holograms and advancements in automated image digitalization and analysis make real-time laser holographic interferometry feasible for two-dimensional flows such as airfoil flows. Typical airfoil measurements would include airfoil pressure distributions, wake and boundary layer profiles, and flow field density contours. This paper addresses some of the problems and requirements of a real-time laser holographic interferometer.
Lee, George↗
Detection of defects in atomic-resolution images of materials using cycle analysis
The automated detection of defects in high-angle annular dark-field Z-contrast (HAADF) scanning-transmission-electron microscopy (STEM) images has been a major challenge. Here, we report an approach for the automated detection and categorization of structural defects based on changes in the material’s local atomic geometry. The approach applies geometric graph theory to the already-found positions of atomic-column centers and is capable of detecting and categorizing any defect in thin diperiodic structures (i.e., “2D materials”) and a large subset of defects in thick diperiodic structures (i.e., 3D or bulk-like materials). Despite the somewhat limited applicability of the approach in detecting and categorizing defects in thicker bulk-like materials, it provides potentially informative insights into the presence of defects. The categorization of defects can be used to screen large quantities of data and to provide statistical data about the distribution of defects within a material. This methodology is applicable to atomic column locations extracted from any type of high-resolution image, but here we demonstrate it for HAADF STEM images.
36 MATERIALS SCIENCE↗
MSTB 2 x 6-Inch Low Speed Tunnel Turbulence Generator Grid/Honeycomb PIV Measurements and Analysis
An assessment of the turbulence levels present in the Measurement Science and Technology (MSTB) branch's 2 x 6-inch low speed wind tunnel was made using Particle Image Velocimetry (PIV), and a turbulence generator consisting of a grid/honeycomb structure. Approximately 3000 digital PIV images were captured and analyzed covering an approximate 2 x 6-inch area along the centerline of the tunnel just beyond the turbulence generator system. Custom software for analysis and acquisition was developed for semi-automated digital PIV image acquisition and analysis. Comparisons between previously obtained LTA and LV turbulence measurements taken in the tunnel are presented.
Blackshire, James L.↗
BATSE imaging survey of the Galactic plane
The burst and transient source experiment (BATSE) onboard the Compton Gamma Ray Observatory (CGRO) provides all sky monitoring capability, occultation analysis and occultation imaging which enables new and fainter sources to be searched for in relatively crowded fields. The occultation imaging technique is used in combination with an automated BATSE image scanner, allowing an analysis of large data sets of occultation images for detections of candidate sources and for the construction of source catalogs and data bases. This automated image scanner system is being tested on archival data in order to optimize the search and detection thresholds. The image search system, its calibration results and preliminary survey results on archival data are reported on. The aim of the survey is to identify a complete sample of black hole candidates in the galaxy and constrain the number of black hole systems and neutron star systems.
Grindlay, J. E.↗
Automated bubble analysis of high-speed subcooled flow boiling images using U-net transfer learning and global optical flow
Capturing and analyzing the bubble dynamics is crucial to improving the understanding of boiling heat transfer mechanisms and predicting boiling heat transfer coefficient and boiling crisis. High speed video (HSV) imaging has been used for decades towards this end. Still, there is no universal approach to quantitatively analyze bubble dynamics from HSV images. In this study, we propose a data-driven post-processing approach to segment, track, and identify wall-attached vapor bubbles from HSV images of the boiling process in subcooled flow conditions. Firstly, we employ a transfer learning framework with a U-Net-based convolution neural network (CNN) architecture to detect and segment bubbles in HSV images of diverse contrast and surface texture using very little data (e.g., 10 images) for training. Then, we evaluate the trained CNN model with 100 ground-truth images, and the validation results show that the model accuracy and precision in detecting the optical footprint of bubbles are higher than 90%. Finally, we suggest a criterion to identify a condensing bubble based on the divergence of the bubble displacement, which is calculated from sequential segmented bubble images using a global optical flow code. Using this combination of machine learning and optical flow, we can identify nucleation sites and track the growth of bubbles nucleating at each site to quantify nucleation site density, nucleation frequency, and other fundamental boiling parameters. The proposed system is validated using results obtained on a special heater, which enables both infrared (IR) thermometry and HSV imaging on a metallic surface. We compare the fundamental boiling parameters obtained by the two different diagnostics. The results show good agreement. In conclusion, the difference between the measurements of nucleation site density, averaged nucleation frequency, and averaged growth time performed with the two techniques is always within ± 20% and mostly ± 10% of the values measured with IR thermometry.
42 ENGINEERING↗