Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “segmental refinement”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

NanoPSD: A software for automatic detection of Nano-Particle Shape Distribution in electron microscopy images

Accurate quantification of the size and morphology of nanoparticles from electron microscopy (EM) images is essential to understand growth mechanisms, surface reactivity, and functional behavior in nanoscale materials. Manual analysis remains slow, subjective, and difficult to reproduce in large datasets. We introduce NanoPSD (Nano-Particle Shape Distribution), an open-source and fully automated framework for quantitative particle detection and morphology analysis from EM images. NanoPSD integrates adaptive contrast enhancement, polarity-agnostic scale-bar detection, Optical Character Recognition (OCR)-based calibration, and classical segmentation via Otsu thresholding with morphological refinement. Particle contours are used to extract geometric descriptors, including equivalent circular diameter, aspect ratio, circularity, and solidity, enabling automated classification into spherical, rod-like, and aggregate morphologies. The framework supports both single-image and batch processing, generating publication-quality visualizations, LaTeX-ready tables, and structured comma-separated values (CSV) datasets. As a demonstration, we applied NanoPSD to plasma-synthesized nanoparticle samples diagnosed via transmission electron microscopy (TEM). The code produced statistically robust size and morphology distributions spanning a few to tens of nanometers with minimal user supervision. The pipeline demonstrates high reproducibility and scalability, processing large image collections with consistent calibration and output formatting. Its modular design enables seamless integration of future deep-learning-based segmentation models, providing a pathway toward intelligent, data-driven electron microscopy analysis.

36 MATERIALS SCIENCE↗
NASA NTRS1992

Interactive explorations of hierarchical segmentations

The authors report on the implementation of an interactive tool, called HSEGEXP, to interactively explore the hierarchical segmentation produced by the iterative parallel region growing (IPRG) algorithm to select the best segmentation result. This combination of the HSEGEXP tool with the IPRG algorithm amounts to a computer-assisted image segmentation system guided by human interaction. The initial application of the HSEGEXP tool is in the refinement of ground reference data based on the IPRG/HSEGEXP segmentation of the corresponding remotely sensed image data. The HSEGEXP tool is being used to help evaluate the effectiveness of an automatic 'best' segmentation process under development.

Tilton, James C.↗
DOE OSTI2015

Automated quantitative 3D analysis of aorta size, morphology, and mural calcification distributions

Purpose: The purpose of this work is to develop a fully automated pipeline to compute aorta morphology and calcification measures in large cohorts of CT scans that can be used to investigate the potential of these measures as imaging biomarkers of cardiovascular disease. Methods: The first step of the automated pipeline is aorta segmentation. The algorithm the authors propose first detects an initial aorta boundary by exploiting cross-sectional circularity of aorta in axial slices and aortic arch in reformatted oblique slices. This boundary is then refined by a 3D level-set segmentation that evolves the boundary to the location of nearby edges. The authors then detect the aortic calcifications with thresholding and filter out the false positive regions due to nearby high intensity structures based on their anatomical location. The authors extract the centerline and oblique cross sections of the segmented aortas and compute the aorta morphology and calcification measures of the first 2500 subjects from COPDGene study. These measures include volume and number of calcified plaques and measures of vessel morphology such as average cross-sectional area, tortuosity, and arch width. Results: The authors computed the agreement between the algorithm and expert segmentations on 45 CT scans and obtained a closest point mean error of 0.62 ± 0.09 mm and a Dice coefficient of 0.92 ± 0.01. The calcification detection algorithm resulted in an improved true positive detection rate of 0.96 compared to previous work. The measurements of aorta size agreed with the measurements reported in previous work. The initial results showed associations of aorta morphology with calcification and with aging. These results may indicate aorta stiffening and unwrapping with calcification and aging. Conclusions: The authors have developed an objective tool to assess aorta morphology and aortic calcium plaques on CT scans that may be used to provide information about the presence of cardiovascular disease and its clinical impact in smokers.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

The grain refinement mechanisms of various phases in shot-peened Nickel-Aluminum bronze (NAB) alloy

Highlights: • There exist two grain refinement mechanisms at the same time in the α phase. • The refinement of β' phase is attributed to the formation of dense and ordered nano twins. • The brittle strip-shape κ{sub iii} phases are fragmented after large deformation. • The κ{sub iv} and κ{sub ii} phases exhibit the twinning and dislocation deformation modes, separately. • The microhardness increases significantly owing to work hardening effect and grain refinement. - Abstract: In this study, coarse-grained Nickel-Aluminum bronze (NAB) alloy is subjected to shot-peening (SP) treatment, and the grain refinement mechanisms of various phases in shot-peened NAB alloy are investigated separately. The results show that severe deformation layers are produced on the surface. Meanwhile, refined grains with the average size below 100 nm are induced through SP treatment indicated by the results of X-ray diffraction (XRD) patterns. Various phases in NAB alloy including α, β' and three intermetallic phases (κ{sub ii}, κ{sub iii} and κ{sub iv}) display different grain refinement mechanisms. The grain refinement in the α phase involves dislocation segmentation in the precipitation-free zone and the twinning intersection in the precipitation zone. The formation of nanotwins by emitting partial dislocations at the grain boundary is found to be responsible for the grain refinement in the β' phase. As for the κ phases, the strip-shaped κ{sub iii} phases are fragmented after severe deformation, and the κ{sub iv} and κ{sub ii} phases exhibit the twinning and dislocation deformation modes, respectively. Also, the microhardness is significantly enhanced after SP treatment, which is mainly attributed to the work hardening effect and grain refinement.

36 MATERIALS SCIENCE↗
NASA NTRS2016

The Segmented Aperture Interferometric Nulling Testbed (SAINT) I: Overview and Air-side System Description

This work presents an overview of the This work presents an overview of the Segmented Aperture Interferometric Nulling Testbed (SAINT), a project that will pair an actively-controlled macro-scale segmented mirror with the Visible Nulling Coronagraph (VNC). SAINT will incorporate the VNCs demonstrated wavefront sensing and control system to refine and quantify the end-to-end system performance for high-contrast starlight suppression. This pathfinder system will be used as a tool to study and refine approaches to mitigating instabilities and complex diffraction expected from future large segmented aperture telescopes., a project that will pair an actively-controlled macro-scale segmented mirror with the Visible Nulling Coronagraph (VNC). SAINT will incorporate the VNCs demonstrated wavefront sensing and control system to refine and quantify the end-to-end system performance for high-contrast starlight suppression. This pathfinder system will be used as a tool to study and refine approaches to mitigating instabilities and complex diffraction expected from future large segmented aperture telescopes.

wavefront sensing↗
DOE OSTI2014

SU-E-J-168: Automated Pancreas Segmentation Based On Dynamic MRI

Purpose: MRI guided radiotherapy is particularly attractive for abdominal targets with low CT contrast. To fully utilize this modality for pancreas tracking, automated segmentation tools are needed. A hybrid gradient, region growth and shape constraint (hGReS) method to segment 2D upper abdominal dynamic MRI is developed for this purpose. Methods: 2D coronal dynamic MR images of 2 healthy volunteers were acquired with a frame rate of 5 f/second. The regions of interest (ROIs) included the liver, pancreas and stomach. The first frame was used as the source where the centers of the ROIs were annotated. These center locations were propagated to the next dynamic MRI frame. 4-neighborhood region transfer growth was performed from these initial seeds for rough segmentation. To improve the results, gradient, edge and shape constraints were applied to the ROIs before final refinement using morphological operations. Results from hGReS and 3 other automated segmentation methods using edge detection, region growth and level set were compared to manual contouring. Results: For the first patient, hGReS resulted in the organ segmentation accuracy as measure by the Dices index (0.77) for the pancreas. The accuracy was slightly superior to the level set method (0.72), and both are significantly more accurate than the edge detection (0.53) and region growth methods (0.42). For the second healthy volunteer, hGReS reliably segmented the pancreatic region, achieving a Dices index of 0.82, 0.92 and 0.93 for the pancreas, stomach and liver, respectively, comparing to manual segmentation. Motion trajectories derived from the hGReS, level set and manual segmentation methods showed high correlation to respiratory motion calculated using a lung blood vessel as the reference while the other two methods showed substantial motion tracking errors. hGReS was 10 times faster than level set. Conclusion: We have shown the feasibility of automated segmentation of the pancreas anatomy based on dynamic MRI.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2017

Intelligent multi-spectral IR image segmentation

We present a neural network based multi-­‐spectral image segmentation method. A neural network is trained on the selected features of both the objects and background in the longwave (LW) Infrared (IR) images. Multiple iterations of training are performed until the accuracy of the segmentation reaches satisfactory level. The segmentation boundary of the LW image is used to segment the midwave (MW) and shortwave (SW) IR images. A second neural network detects the local discontinuities and refines the accuracy of the local boundaries. The neural net based segmentation method is compared with Wavelet-­‐threshold and Grab-­‐Cut methods. Test results have shown increased accuracy and robustness of this segmentation scheme for multi-­‐spectral IR images.

Torres, Gilbert↗
DOE OSTI2019

PRIMA-X - Performance Retargeting of Instrumentation, Measurement, and Analysis Technologies for Exascale Computing

The PRIMA-X (Performance Retargeting of Instrumentation, Measurement, and Analysis Technologies for Exascale Computing) project is the successor of the DOE PRIMA (Performance Refactoring of Instrumentation, Measurement, and Analysis Technologies for Petascale Computing) project, which addressed the challenge of creating a core measurement infrastructure that would serve as a common platform for both integrating leading parallel performance systems (notably TAU and Scalasca) and developing next-generation scalable performance tools. The PRIMA-X project shifts the focus away from refactorization of robust performance tools towards a re-targeting of the parallel performance measurement and analysis architecture for extreme scales. The massive concurrency, asynchronous execution dynamics, hardware heterogeneity, and multi-objective prerequisites (performance, power, resilience) that identify exascale systems introduce fundamental constraints on the ability to carry forward existing performance methodologies. In particular, there must be a deemphasis of per-thread observation techniques to significantly reduce the otherwise unsustainable flood of redundant performance data. Instead, it will be necessary to assimilate multi-level resource observations into macroscopic performance views, from which resilient performance metrics can be attributed to the computational features of the application. This requires a scalable framework for node-level and system-wide monitoring and runtime analyses of dynamic performance information. Also, the interest in optimizing parallelism parameters with respect to performance and energy drives the integration of tool capabilities in the exascale environment further. Initially, PRIMA-X was a collaborative project between the University of Oregon (lead institution) and the German Research School for Simulation Sciences (GRS). Because Prof. Wolf, the PI at GRS, accepted a position as full professor at Technische Universitaet Darmstadt (TU Darmstadt) starting February 1st, 2015, the work of GRS was continued at TU Darmstadt. This report covers the work at TU Darmstadt after the transition. The first main accomplishment of TU Darmstadt is the development of different techniques to aggregate performance data on the level of threads. TU Darmstadt evaluated different schemes designed at GRS during the first phase of this project and integrated them into Score-P, a widely used performance measurement framework for HPC applications. The second main accomplishment is a substantial increase of Score-P’s scalability, achieved by improving its internal representation of the underlying system. Third, we extended our performance-modeling tool Extra-P to automatically create models with more than one input parameter (e.g., process count and input size), which was previously impossible. These models allow the performance behavior of an application to be extrapolated to larger machines. Specific applications of the new multi-parameter capability realized in this project include modeling the isoefficiency function of task-based applications and a novel co-design methodology. Further extensions of Extra-P comprise online modeling functionality, automatic refinement of the model search space, and segmented performance modeling. Further contributions include new locality metrics for both shared- and distributed-memory applications, a new way of measuring the impact of inter-application inference on the execution time of an application, and finally an automatic method that selects appropriate OpenMP constructs for the parallelization of sequential code and classifies the affected variables based on their sharing semantics.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2011

Method for Identifying Probable Archaeological Sites from Remotely Sensed Data

Archaeological sites are being compromised or destroyed at a catastrophic rate in most regions of the world. The best solution to this problem is for archaeologists to find and study these sites before they are compromised or destroyed. One way to facilitate the necessary rapid, wide area surveys needed to find these archaeological sites is through the generation of maps of probable archaeological sites from remotely sensed data. We describe an approach for identifying probable locations of archaeological sites over a wide area based on detecting subtle anomalies in vegetative cover through a statistically based analysis of remotely sensed data from multiple sources. We further developed this approach under a recent NASA ROSES Space Archaeology Program project. Under this project we refined and elaborated this statistical analysis to compensate for potential slight miss-registrations between the remote sensing data sources and the archaeological site location data. We also explored data quantization approaches (required by the statistical analysis approach), and we identified a superior data quantization approached based on a unique image segmentation approach. In our presentation we will summarize our refined approach and demonstrate the effectiveness of the overall approach with test data from Santa Catalina Island off the southern California coast. Finally, we discuss our future plans for further improving our approach.

Tilton, James C.↗
DOE OSTI2025

Accurate segmentation of localized corrosion in structural alloys via deep learning

This study presents a deep learning-based approach for the automated segmentation of corrosion damage in scanning electron microscopy (SEM) images. The proposed method enables rapid and accurate segmentation of corrosion features in these SEM images, making it highly suitable for real-time applications such as automated microscopy. Specifically, a dedicated corrosion segmentation database tailored for this task is constructed. The newly constructed dataset, alongside data from two public databases, are employed to jointly train a deep learning-based model modified with a texture refinement module. Compared to the same model without the texture refinement module, the refined model substantially enhances the efficacy and efficiency of corrosion segmentation. Furthermore, the methodology developed here is extendable to segmentation tasks for other materials with similar resolution, texture, and contrast characteristics, thereby paving the way for accelerated and automated analysis in corrosion science and beyond.

Artificial Intelligence↗
DOE OSTI2015

Brickworx builds recurrent RNA and DNA structural motifs into medium- and low-resolution electron-density maps

A computer program that builds crystal structure models of nucleic acid molecules is presented. Brickworx is a computer program that builds crystal structure models of nucleic acid molecules using recurrent motifs including double-stranded helices. In a first step, the program searches for electron-density peaks that may correspond to phosphate groups; it may also take into account phosphate-group positions provided by the user. Subsequently, comparing the three-dimensional patterns of the P atoms with a database of nucleic acid fragments, it finds the matching positions of the double-stranded helical motifs (A-RNA or B-DNA) in the unit cell. If the target structure is RNA, the helical fragments are further extended with recurrent RNA motifs from a fragment library that contains single-stranded segments. Finally, the matched motifs are merged and refined in real space to find the most likely conformations, including a fit of the sequence to the electron-density map. The Brickworx program is available for download and as a web server at http://iimcb.genesilico.pl/brickworx.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2016

3D exemplar-based random walks for tooth segmentation from cone-beam computed tomography images

Purpose: Tooth segmentation is an essential step in acquiring patient-specific dental geometries from cone-beam computed tomography (CBCT) images. Tooth segmentation from CBCT images is still a challenging task considering the comparatively low image quality caused by the limited radiation dose, as well as structural ambiguities from intercuspation and nearby alveolar bones. The goal of this paper is to present and discuss the latest accomplishments in semisupervised tooth segmentation with adaptive 3D shape constraints. Methods: The authors propose a 3D exemplar-based random walk method of tooth segmentation from CBCT images. The proposed method integrates semisupervised label propagation and regularization by 3D exemplar registration. To begin with, the pure random walk method is to get an initial segmentation of the teeth, which tends to be erroneous because of the structural ambiguity of CBCT images. And then, as an iterative refinement, the authors conduct a regularization by using 3D exemplar registration, as well as label propagation by random walks with soft constraints, to improve the tooth segmentation. In the first stage of the iteration, 3D exemplars with well-defined topologies are adapted to fit the tooth contours, which are obtained from the random walks based segmentation. The soft constraints on voxel labeling are defined by shape-based foreground dentine probability acquired by the exemplar registration, as well as the appearance-based probability from a support vector machine (SVM) classifier. In the second stage, the labels of the volume-of-interest (VOI) are updated by the random walks with soft constraints. The two stages are optimized iteratively. Instead of the one-shot label propagation in the VOI, an iterative refinement process can achieve a reliable tooth segmentation by virtue of exemplar-based random walks with adaptive soft constraints. Results: The proposed method was applied for tooth segmentation of twenty clinically captured CBCT images. Three metrics, including the Dice similarity coefficient (DSC), the Jaccard similarity coefficient (JSC), and the mean surface deviation (MSD), were used to quantitatively analyze the segmentation of anterior teeth including incisors and canines, premolars, and molars. The segmentation of the anterior teeth achieved a DSC up to 98%, a JSC of 97%, and an MSD of 0.11 mm compared with manual segmentation. For the premolars, the average values of DSC, JSC, and MSD were 98%, 96%, and 0.12 mm, respectively. The proposed method yielded a DSC of 95%, a JSC of 89%, and an MSD of 0.26 mm for molars. Aside from the interactive definition of label priors by the user, automatic tooth segmentation can be achieved in an average of 1.18 min. Conclusions: The proposed technique enables an efficient and reliable tooth segmentation from CBCT images. This study makes it clinically practical to segment teeth from CBCT images, thus facilitating pre- and interoperative uses of dental morphologies in maxillofacial and orthodontic treatments.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

SU-E-T-41: Analysis of GI Dose Variability Due to Intrafraction Setup Variance

Purpose: Proton SBRT (stereotactic body radiation therapy) can be an effective modality for treatment of gastrointestinal tumors, but limited in practice due to sensitivity with respect to variation in the RPL (radiological path length). Small, intrafractional shifts in patient anatomy can lead to significant changes in the dose distribution. This study describes a tool designed to visualize uncertainties in radiological depth in patient CT's and aid in treatment plan design. Methods: This project utilizes the Shadie toolkit, a GPU-based framework that allows for real-time interactive calculations for volume visualization. Current SBRT simulation practice consists of a serial CT acquisition for the assessment of inter- and intra-fractional motion utilizing patient specific immobilization systems. Shadie was used to visualize potential uncertainties, including RPL variance and changes in gastric content. Input for this procedure consisted of two patient CT sets, contours of the desired organ, and a pre-calculated dose. In this study, we performed rigid registrations between sets of 4DCT's obtained from a patient with varying setup conditions. Custom visualizations are written by the user in Shadie, permitting one to create color-coded displays derived from a calculation along each ray. Results: Serial CT data acquired on subsequent days was analyzed for variation in RPB and gastric content. Specific shaders were created to visualize clinically relevant features, including RPL (radiological path length) integrated up to organs of interest. Using pre-calculated dose distributions and utilizing segmentation masks as additional input allowed us to further refine the display output from Shadie and create tools suitable for clinical usage. Conclusion: We have demonstrated a method to visualize potential uncertainty for intrafractional proton radiotherapy. We believe this software could prove a useful tool to guide those looking to design treatment plans least insensitive to motion for patients undergoing proton SBRT in the GI tract.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1984

Volumetric representation for object model acquisition

This paper describes a volumetric representation that has been specified for use in a system that derives object models from a sequence of images with viewpoint specifications. This volumetric representation is an enumerative structure referred to here as a volume segment representation. The model acquisition system was designed to form an initial object approximation, then refine that approximation as subsequent information is obtained. The refinement process requires a readily modifiable object representation, while the overall goal of the system demands a representation descriptive of surface detail. The volume segment structure provides both of those capabilities.

Martin, W. N.↗
DOE OSTI2018

Transport in Random Media With Inhomogeneous Mixing Statistics

The Levermore-Pomraning (LP) model has been investigated for use in implicit Monte Carlo (IMC) algorithms to describe thermal photon transport computations in stochastically mixed cells with material energy coupling. For purely absorbing non-emitting materials, the LP-model is known to predict the exact material averaged fluxes when the mixing statistics are Markovian. In the presence of scattering or reemmission, or if the mixing is non-Markovian, the LP-model is approximate and has greatly diminished accuracy for optically thick, highly scattering materials. Nevertheless, the model enables a robust extension of the simple atomic mix approximation to accommodate spatial correlation effects through well characterized parameters such as mean chord lengths. In this work, we construct exact analytical solutions of the LP-equations for piecewise constant spatial variation of the material chord lengths. It is not possible in general to obtain closed form solutions when the chord lengths are continuous functions of space, although we briefly examine special conditions under which this is feasible. However, by allowing an arbitrarily fine spatial segmentation of the medium it is possible to construct highly refined solutions to fit desired spatial chord length profiles. Material volume fractions that are consistent with the chord lengths are first constructed by solving the associated Chapmann-Kolmogorov equations and applied to obtain the material averaged fluxes in two settings: when the materials are purely absorbing, and when scattering is present in rod model geometry. Illustrative numerical results for the volume fractions and material averaged fluxes are obtained by implementing the algorithms in MATLAB for spatially linear and quadratic variations of the chord lengths.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1981

Development of a composite geodetic structure for space construction, phase 2

Primary physical and mechanical properties were defined for pultruded hybrid HMS/E-glass P1700 rod material used for the fabrication of geodetic beams. Key properties established were used in the analysis, design, fabrication, instrumentation, and testing of a geodetic parameter cylinder and a lattice cone closeout joined to a short cylindrical geodetic beam segment. Requirements of structural techniques were accomplished. Analytical procedures were refined and extended to include the effect of rod dimensions for the helical and longitudinal members on local buckling, and the effect of different flexural and extensional moduli on general instability buckling.

Source record↗
DOE OSTI2026

Creating a Training Dataset for Semantic Segmentation of Canal Networks for Irrigation Modernization

Canal infrastructure has provided critical irrigation water to the western United States for over a century. To continue providing vital water resources to the semi-arid West, irrigation systems must undergo maintenance and modernization. Many canal companies are resource-constrained, and because funding opportunities often require detailed knowledge of existing infrastructure, they can struggle to secure financial capital. We address this problem by creating training data for a semantic segmentation deep learning model to map canal networks throughout the western United States. To create a diverse and robust training dataset, we labelled 1-m NAIP imagery with the locations of no canals, wet canals, and dry/vegetated canals. Since creating these datasets is time consuming, we first developed a preprocessing methodology to identify canals within our four study areas. We used NAIP imagery and provided canal centerline data to buffer, standardize, and cluster the imagery, automating the labeling process as much as possible. However, this still required manual cleaning and manual classification of canal type. Challenges arose when canals were interrupted (e.g., road culverts or piped sections) or when nearby features shared similar characteristics (e.g., irrigated fields, trees, and shadows). Combining automated preprocessing with manual refinement produced four detailed canal masks to be used in the semantic segmentation model developed by Richard Tapia.

13 - HYDRO ENERGY↗
DOE OSTI2015

Microstructural characteristics of adiabatic shear localization in a metastable beta titanium alloy deformed at high strain rate and elevated temperatures

The microstructural evolution and grain refinement within adiabatic shear bands in the Ti6554 alloy deformed at high strain rates and elevated temperatures have been characterized using transmission electron microscopy. No stress drops were observed in the corresponding stress–strain curve, indicating that the initiation of adiabatic shear bands does not lead to the loss of load capacity for the Ti6554 alloy. The outer region of the shear bands mainly consists of cell structures bounded by dislocation clusters. Equiaxed subgrains in the core area of the shear band can be evolved from the subdivision of cell structures or reconstruction and transverse segmentation of dislocation clusters. It is proposed that dislocation activity dominates the grain refinement process. The rotational recrystallization mechanism may operate as the kinetic requirements for it are fulfilled. The coexistence of different substructures across the shear bands implies that the microstructural evolution inside the shear bands is not homogeneous and different grain refinement mechanisms may operate simultaneously to refine the structure. - Graphical abstract: Display Omitted - Highlights: • The microstructure within the adiabatic shear band was characterized by TEM. • No stress drops were observed in the corresponding stress–strain curve. • Dislocation activity dominated the grain refinement process. • The kinetic requirements for rotational recrystallization mechanism were fulfilled. • Different grain refinement mechanisms operated simultaneously to refine the structure.

36 MATERIALS SCIENCE↗