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Results for “mathematical morphology”

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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Mathematical Morphological Filtering with a Self-Adaptive Reconstruction Technique and Application to Local Seismic Data

Recorded seismic data are generally contaminated by noise from different sources, which masks the signals of interest. In the seismology community, frequency filtering (FF) is the standard method for noise suppression. However, when the signal of interest and noise share the same frequency band, the latter cannot be filtered out without infringing on the former. We implemented a noise suppression approach based on the mathematical morphology theorem. The method involves compound operations of dilation and erosion using structuring elements of varying lengths and decomposes an input noisy waveform into several time functions with differing characteristics. Further, the filtered waveform is constructed from the time functions using a self-adaptive reconstruction technique. Application to a data set of >4700 local waveforms suggests that the implemented mathematical morphological filtering (MMF) approach is efficient for data with low signal-to-noise ratio (SNR) and significantly outperforms FF in that SNR range. For most of the dataset, FF, machine learning (ML) denoising, and continuous wavelet transform (CWT) thresholding result in higher SNR values compared with the MMF method. However, for ~42% of the waveforms, MMF outperforms FF, and the SNR gain achieved with MMF is as large as ~23 dB. Compared to ML denoising and CWT thresholding, this proportion drops to only ~10%–14%. Our results suggests that in an operational setting, MMF cannot replace the other noise suppression methods; however, signal detection can be improved if MMF is used to supplement them in some scenarios. MMF could help detect signals in problematic low-SNR data, which are currently being missed particularly when using FF alone.

58 GEOSCIENCES↗
DOE OSTI2014

Morphological Analysis of Zirconium Nuclear Fuel Retaining Rods Braided with SiC: Quality Assurance and Defect Identification

In the after-Fukushima world, the stability of materials under extreme conditions is an important issue for the safety of nuclear reactors. Among the methods explored currently to improve zircaloys’ thermal stability in off-normal conditions, using a protective coat of the SiC filaments is considered because silicon carbide is well known for its remarkable chemical inertness at high temperatures. A typical SiC fiber contains ~50,000 individual filaments of 5 – 10 µm in diameter. In this paper, an effort was made to develop and apply mathematical morphology to the process of automatic defect identification in Zircaloy-4 rods braided with the protective layer of the silicon carbide filament. However, the issues of the braiding quality have to be addressed to ensure its full protective potential. We present the original mathematical morphology algorithms that allow solving this problem of quality assurance successfully. In nuclear industry, such algorithms are used for the first time, and could be easily generalized to the case of automated continuous monitoring for defect identification in the future.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2014

WE-D-9A-04: Improving Multi-Modality Image Registration Using Edge-Based Transformations

Purpose: Multi-modality deformable image registration (DIR) for head and neck (HN) radiotherapy is difficult, particularly when matching computed tomography (CT) scans with magnetic resonance imaging (MRI) scans. We hypothesized that the ‘shared information’ between images of different modalities was to be found in some form of edge-based transformation, and that novel edge-based DIR methods might outperform standard DIR methods. Methods: We propose a novel method that combines gray-scale edge-based morphology and mutual information (MI) in two stages. In the first step, we applied a modification of a previously published mathematical morphology method as an efficient gray scale edge estimator, with denoising function. The results were fed into a MI-based solver (plastimatch). The method was tested on 5 HN patients with pretreatment CT and MR datasets and associated follow-up weekly MR scans. The followup MRs showed significant regression in tumor and normal structure volumes as compared to the pretreatment MRs. The MR images used in this study were obtained using fast spin echo based T2w images with a 1 mm isotropic resolution and FOV matching the CT scan. Results: In all cases, the novel edge-based registration method provided better registration quality than MI-based DIR using the original CT and MRI images. For example, the mismatch in carotid arteries was reduced from 3–5 mm to within 2 mm. The novel edge-based method with different registration regulation parameters did not show any distorted deformations as compared to the non-realistic deformations resulting from MI on the original images. Processing time was 1.3 to 2 times shorter (edge vs. non-edge). In general, we observed quality improvement and significant calculation time reduction with the new method. Conclusion: Transforming images to an ‘edge-space,’ if designed appropriately, greatly increases the speed and accuracy of DIR.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

A Measurement Source Authentication Methodology for Power System Cyber Security Enhancement

This letter proposes a spatial signature-based power system measurement source identification and authentication methodology. A mathematical morphology method is used to decompose the power system measurement signals and obtain the intrinsic components, which sparsity trends and roughness values are further derived to establish the time-frequency (TF) sparsity mapping. Then random forest classification is utilized to correlate the correct measurement source for each measurement signal based on the derived TF sparsity mapping. Finally, experiment results using five phasor measurement units has validated the effectiveness of this methodology.

42 ENGINEERING↗
DOE OSTI2024

Jet classification using high-level features from anatomy of top jets

Recent advancements in deep learning models have significantly enhanced jet classification performance by analyzing low-level features (LLFs). However, this approach often leads to less interpretable models, emphasizing the need to understand the decision-making process and to identify the high-level features (HLFs) crucial for explaining jet classification. To address this, we consider the top jet tagging problems and introduce an analysis model (AM) that analyzes selected HLFs designed to capture important features of top jets. Our AM mainly consists of the following three modules: a relation network analyzing two-point energy correlations, mathematical morphology and Minkowski functionals for generalizing jet constituent multiplicities, and a recursive neural network analyzing subjet constituent multiplicity to enhance sensitivity to subjet color charges. We demonstrate that our AM achieves performance comparable to the Particle Transformer (ParT) while requiring fewer computational resources in a comparison of top jet tagging using jets simulated at the hadronic calorimeter angular resolution scale. Furthermore, as a more constrained architecture than ParT, the AM exhibits smaller training uncertainties because of the bias-variance tradeoff. We also compare the information content of AM and ParT by decorrelating the features already learned by AM. Lastly, we briefly comment on the results of AM with finer angular resolution inputs.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2014

Improving the precision of astrometry for space debris

The data reduction method for optical space debris observations has many similarities with the one adopted for surveying near-Earth objects; however, due to several specific issues, the image degradation is particularly critical, which makes it difficult to obtain precise astrometry. An automatic image reconstruction method was developed to improve the astrometry precision for space debris, based on the mathematical morphology operator. Variable structural elements along multiple directions are adopted for image transformation, and then all the resultant images are stacked to obtain a final result. To investigate its efficiency, trial observations are made with Global Positioning System satellites and the astrometry accuracy improvement is obtained by comparison with the reference positions. The results of our experiments indicate that the influence of degradation in astrometric CCD images is reduced, and the position accuracy of both objects and stellar stars is improved distinctly. Our technique will contribute significantly to optical data reduction and high-order precision astrometry for space debris.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Source Authentication of Distribution Synchrophasors for Cybersecurity of Microgrids

This letter proposes a hybrid approach combining Self-Adaptive Mathematical Morphology (SAMM) and Time-Frequency (TF) techniques to authenticate the source information of Distribution Synchrophasors (DS) within near-range locations. The SAMM can adaptively regulate the synchrophasors variations which are representatives of local environmental characteristics. Subsequently, TF mapping is employed to extract informative signatures from the regulated synchrophasors variation. Finally, Random Forest Classification (RFC) is used to correlate the extracted signatures with the source information based on the derived TF mapping. Experiment results using DS collected at multiple small geographical scales validated the proposed methodology.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2015

Morpho-Hawk

Morpho-Hawk applies projectional mathematical morphology in a fundamentally new way. Morpho-Hawk extracts object information from digital images by filtering with simple geometrical figures such as rectangles or simple curves. Two core algorithms are used to accomplish this: 1) Object Detection and Feature Extraction from Images: Using projectional morphology, Morpho-Hawk finds features of interest within an image, such as contours, shapes, colors, infrared spectra, and more. Because Morpho-Hawk identifies features based on shape or form, the method can uniquely handle different image or object conditions and directions. MorphoHawk can evaluate all possible images of the analyzed scene using the same transformations that are applied just to one image of that scene. Hence, recognizing a defect within an image or part of an image provides information to assist in recognizing other objects in the image. In addition, known background image information can be morphologically subtracted out in order to focus on the appearance or disappearance of the features of interest. 2) Object Analysis: Upon detection of an object of interest, Morpho-Hawk can analyze the object based learned information from prior images or end-user defined criteria for certain object of special interest by selecting feature (e.g., color, size, shape, apparent volume) and/or symbol (e.g., letter, number). The novel algorithm can analyze targeted objects, even as conditions of such as illumination, shadows, and spectral regions change strongly in following images as compared to the original image. This enables robust recognition, interpolation and prediction. As the analysis shows the presence of these features in the optical signal, the algorithm can make a determination based on user defined probability. The object information can be stored for later analysis using simple morphological data compression methods and/or retention of the original optical images.

Glazoff, MichaelV.↗
DOE OSTI2019

3D Morphological Analysis and Synthesis of Industrial Materials Surfaces

We present the original results pertaining to understanding topographical features on real industrial alloy surfaces. Quite often, such materials are subjected to different thermomechanical treatments with the goal of improving their yield strength, ductility, corrosion resistance, and other properties. Operations of casting, rolling, extruding, and stretching metal invariably leave their distinct “signatures” on the processed materials’ surfaces. It is highly desirable to be able to perform their characterization not in terms of the numerous surface roughness and/or waviness parameters, but rather in terms of the distinctive contributions of such operations. Indeed, if such a quantitative characterization were possible, it would be much easier to introduce corrective actions into the appropriate chain of production operations. We provide such possibility with the “morphological analysis and synthesis” techniques described in the paper. The results can be quantified either using surface topography measurements or studying light scattering from such surfaces. This last capability also gives a quick check of the “morphological similarity” of the “real” and modeled surfaces (images), which is important, e.g., in phase-field simulations. All calculations were performed using the MorphoHawk© software developed by the author and colleagues.

36 MATERIALS SCIENCE↗
DOE OSTI2012

Using Segmentation in CT Metal Artifact Reduction

Metal artifact reduction methods in computed tomography replace the projection data passing through metals with an estimate of the true data. Inaccurate estimation leads to the generation of secondary artifacts. Data estimates can be improved by the use of prior knowledge of the projection data. In this paper, a method has been created to generate a prior image. The method uses computer vision techniques to segment regions of the initially reconstructed image and then discriminates between regions that are likely to be artifacts and anatomical structures. Results on test images show that metal artifacts are reduced and that few secondary artifacts are present, even in the case of multiple metal objects.

42 ENGINEERING↗
DOE OSTI2021

Overcoming the Challenges to Enhancing Experimental Plant Biology With Computational Modeling

The study of complex biological systems necessitates computational modeling approaches that are currently underutilized in plant biology. Many plant biologists have trouble identifying or adopting modeling methods to their research, particularly mechanistic mathematical modeling. Here we address challenges that limit the use of computational modeling methods, particularly mechanistic mathematical modeling. We divide computational modeling techniques into either pattern models (e.g., bioinformatics, machine learning, or morphology) or mechanistic mathematical models (e.g., biochemical reactions, biophysics, or population models), which both contribute to plant biology research at different scales to answer different research questions. We present arguments and recommendations for the increased adoption of modeling by plant biologists interested in incorporating more modeling into their research programs. As some researchers find math and quantitative methods to be an obstacle to modeling, we provide suggestions for easy-to-use tools for non-specialists and for collaboration with specialists. This may especially be the case for mechanistic mathematical modeling, and we spend some extra time discussing this. Through a more thorough appreciation and awareness of the power of different kinds of modeling in plant biology, we hope to facilitate interdisciplinary, transformative research.

58 GEOSCIENCES↗
DOE OSTI2016

Rheological properties of molten flax- and Tencel{sup ®}-polypropylene composites: Influence of fiber morphology and concentration

The rheological properties of short fiber reinforced polypropylene were investigated. Flax and Tencel{sup ®} are two cellulose based fibers used in this study. Flax fibers are extracted from the bast of plants. They are composed of thin elementary fibers and rigid thick bundles made of elementary fibers “glued” together. Tencel{sup ®} is a man-made cellulosic fiber spun from cellulose solution, with a uniform diameter, thin, and flexible. First, fiber dimensions before and after compounding were analyzed. Both types of fibers were broken during compounding. Flax shows larger length and diameter than Tencel{sup ®}, but aspect ratio of flax is smaller. The reason is that after compounding flax remained in bundles. Dynamic viscosity, elastic and viscous moduli were studied as a function of fiber type, concentration (from 0 to 30 wt. %), and composite temperature (from 180 to 200 °C). All Tencel{sup ®}-based composites showed higher apparent yield stress, viscosity, and moduli compared to flax-based composites at the same fiber concentrations. The results are analyzed in terms of the influence of fiber type, aspect ratio, and flexibility. The importance of considering fiber morphology is demonstrated as far as it controls fiber flexibility and fiber-fiber interactions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Wetting state on hydrophilic and hydrophobic micro-textured surfaces: Thermodynamic analysis and X-ray visualization

In this study, the wetting state on hydrophobic and hydrophilic micro-textured surfaces was investigated. High spatial resolution synchrotron X-ray radiography was used to overcome the limitations in visualization in previous research and clearly visualize the wetting state for each droplet under quantified surface conditions. Based on thermodynamic characteristics, a theoretical model for wetting state depending on the chemical composition (intrinsic contact angle) and geometrical morphology (roughness ratio) of the surfaces was developed.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗