SEARCH · Search NASA

Results for “shape classification”

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.

SourceResearch connectionFrom yearThrough year

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

At least 37 records · Page 2

All sources NASA NTRS DOE OSTI

DOE OSTI2023

AutoSourceID-Classifier: Star-galaxy classification using a convolutional neural network with spatial information

Aims.Traditional star-galaxy classification techniques often rely on feature estimation from catalogs, a process susceptible to introducing inaccuracies, thereby potentially jeopardizing the classification’s reliability. Certain galaxies, especially those not manifesting as extended sources, can be misclassified when their shape parameters and flux solely drive the inference. We aim to create a robust and accurate classification network for identifying stars and galaxies directly from astronomical images. Methods.The AutoSourceID-Classifier (ASID-C) algorithm developed for this work uses 32x32 pixel single filter band source cutouts generated by the previously developed AutoSourceID-Light (ASID-L) code. By leveraging convolutional neural networks (CNN) and additional information about the source position within the full-field image, ASID-C aims to accurately classify all stars and galaxies within a survey. Subsequently, we employed a modified Platt scaling calibration for the output of the CNN, ensuring that the derived probabilities were effectively calibrated, delivering precise and reliable results. Results.We show that ASID-C, trained on MeerLICHT telescope images and using the Dark Energy Camera Legacy Survey (DECaLS) morphological classification, is a robust classifier and outperforms similar codes such as SourceExtractor. To facilitate a rigorous comparison, we also trained an eXtreme Gradient Boosting (XGBoost) model on tabular features extracted by SourceExtractor. While this XGBoost model approaches ASID-C in performance metrics, it does not offer the computational efficiency and reduced error propagation inherent in ASID-C’s direct image-based classification approach. ASID-C excels in low signal-to-noise ratio and crowded scenarios, potentially aiding in transient host identification and advancing deep-sky astronomy.

Astronomy & Astrophysics↗

DOE OSTI2016

Nanomaterials in Extreme Environments: Fundamentals and Applications Rostislav A. Andrievski and Arsen V. Khatchoyan

Nanomaterials in Extreme Environments Rostislav A. Andrievski and Arsen V. Khatchoyan Springer, 2016 106 pages, $99.00 (e-book $69.99) ISBN 978–3-319–25331–2 This slim volume is an extensive review of our current understanding of the response of nanostructured materials to extreme operating conditions, such as high temperature, flux of high energy neutrons, high pressure, mechanical stress, and oxidizing environments. The emphasis is on metallic materials, especially Cu alloys. Graphene-based materials, fullerenes, polymeric materials, nano-glasses and glass-ceramics are not covered by this review. The book has six chapters including an introduction and a brief conclusion. The introduction documents the growth of scientific interest in nanostructured materials and stresses the need to study the behavior of nanomaterials under extreme conditions. This chapter also presents Herbert Gleiter’s classification of nanomaterials into twelve groups based on the shapes of the nanoscale features and chemical composition of the components of the nanostructure. The second chapter deals with the high temperature environment and the thermodynamics and kinetics of grain growth. The authors identify the lack of reliable thermodynamic data as a key limitation in this field. The discussion brings out the interplay of structural relaxation, redistribution of excess free volume, diffusion, and recrystallization in multicomponent nanostructures at elevated temperature. Chapter 3 focuses on the effects of ion and neutron irradiation on the structure and properties of nanomaterials. The authors do a good job of highlighting recent studies on the radiation tolerance of nanocrystalline oxides and rapid grain growth under irradiation. The material addresses both fission and fusion reactor applications. Chapter 4 reviews the effects of severe plastic deformation and cyclic loading on nanostructure formation and phase transformation. This chapter also explores the challenge of achieving high density while retaining nanostructural features during processing under extreme loads and high temperatures. Chapter 5 discusses the effects of corrosion on nanomaterials. The behavior of a variety of alloys and high melting point compounds in liquid media and high temperature oxidizing environments is reviewed. The concluding chapter identifies areas for further research. The book would have benefited from careful copy editing for use of the English language. Moreover, the excessive use of acronyms makes the text difficult to read. Each chapter ends with a section on the application of nanomaterials and a long list of references. The integration of theoretical approaches and simulation results with experimental data offers fresh insights into the behavior of nanomaterials. Overall, this book will serve as useful reference material for researchers interested in nanomaterials driven to extremes. Reviewer: Ram Devanathan is Technical Group Manager of Reactor Materials and Mechanical Design, Pacific Northwest National Laboratory, USA.

Devanathan, Ram↗

DOE OSTI2020

Recycling coins from scrap

A material sorting system sorts materials utilizing a vision system that implements a machine learning system in order to identify or classify each of the materials, which are then sorted into separate groups based on such an identification or classification determining that the materials have a specified geometric shape. Such a system can sort monetary coins or other valuable metals from other forms of scrap.

36 MATERIALS SCIENCE↗

DOE OSTI2019

Gaussian mixture models as automated particle classifiers for fast neutron detectors

Pulse shape discrimination (PSD) is the task of classifying electronic pulse shapes for different particle types such as gamma rays and fast neutrons interacting in scintillators and read out by photo sensitive detectors. This field has been limited in its adoption of techniques found in the statistical learning community. Methods initially employed in the 1960s for analog electronic circuitry persist in the current PSD literature describing operations performed on digitized pulses, which are amenable to statistical rigor. Despite vast amounts of data collected at low energy levels, traditional PSD methods are unable to discriminate particles below a certain threshold. As such, in this work, Gaussian mixture models (GMMs) are used as a clustering technique for fast neutron detection in the absence of labeled data. GMMs yield improvements spanning the energy spectrum in a desirably efficient, unsupervised fashion. An extension, the Dirichlet Process GMM, provides further flexibility and classification improvement.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Dimensionality reduction using elastic measures

With the recent surge in big data analytics for hyperdimensional data, there is a renewed interest in dimensionality reduction techniques. In order for these methods to improve performance gains and understanding of the underlying data, a proper metric needs to be identified. This step is often overlooked, and metrics are typically chosen without consideration of the underlying geometry of the data. Here, in this paper, we present a method for incorporating elastic metrics into the t-distributed stochastic neighbour embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP). We apply our method to functional data, which is uniquely characterized by rotations, parameterization and scale. If these properties are ignored, they can lead to incorrect analysis and poor classification performance. Through our method, we demonstrate improved performance on shape identification tasks for three benchmark data sets (MPEG-7, Car data set and Plane data set of Thankoor), where we achieve 0.77, 0.95 and 1.00 F1 score, respectively.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Frequency Control and Dynamics (Part 1) [Slides]

This presentation provides an introductory overview of system dynamics and frequency control in electric power systems, with a focus on concepts relevant to small and interconnected grids such as those in Malawi. It explains foundational principles of AC system frequency, the relationship between generation-demand balance and frequency deviations, and the operational limits of generators and end-use equipment. The deck discusses frequency stability within broader system stability classifications and illustrates how inertia and turbine-governor dynamics shape system response to disturbances. It then outlines the tiered approach to frequency control - primary, secondary, and tertiary - detailing the roles, characteristics, timescales, and response mechanisms of each. Special emphasis is placed on hydro and thermal unit behavior, area control error (ACE), automatic generation control (AGC), and the operational implications of interconnecting small systems with larger grids. The material was developed to support Malawi's electricity sector and the establishment of the Southern Africa Battery Energy Storage Center of Excellence (SABESS CoE).

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2026

Frequency Control and Dynamics (Part 2) [Slides]

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Decoding Ethiopian Abodes: Towards Classifying Buildings by Occupancy Type Using Footprint Morphology

Building occupancy classification plays a crucial role in urban planning, disaster management, and population modeling. Traditional methods often require extensive field surveys or detailed datasets, which can be time-consuming, expensive, and may yield incomplete or erroneous data. In this paper, we present a novel approach for classifying buildings as residential or non-residential using only building footprint data. By extracting geometric shape derivatives that characterize building morphology, we developed a high-accuracy classification model employing a combination of unsupervised and supervised learning methods. We utilized open-source data from Open Street Map, aggregating it to create binary labels for buildings based on their respective human use type. Our approach demonstrates the potential for scalability without the need for additional data sources other than building footprints and labels, offering a more efficient solution for building occupancy classification.

Adams, Daniel↗

DOE OSTI2017

Computer-Assisted Diagnosis System for Breast Cancer in Computed Tomography Laser Mammography (CTLM)

Computed tomography laser mammography (Eid et al. Egyp J Radiol Nucl Med, 37(1): p. 633–643, 1) is a non-invasive imaging modality for breast cancer diagnosis, which is time-consuming and challenging for the radiologist to interpret the images. Some issues have increased the missed diagnosis of radiologists in visual manner assessment in CTLM images, such as technical reasons which are related to imaging quality and human error due to the structural complexity in appearance. The purpose of this study is to develop a computer-aided diagnosis framework to enhance the performance of radiologist in the interpretation of CTLM images. The proposed CAD system contains three main stages including segmentation of volume of interest (VOI), feature extraction and classification. A 3D Fuzzy segmentation technique has been implemented to extract the VOI. The shape and texture of angiogenesis in CTLM images are significant characteristics to differentiate malignancy or benign lesions. The 3D compactness features and 3D Grey Level Co-occurrence matrix (GLCM) have been extracted from VOIs. Multilayer perceptron neural network (MLPNN) pattern recognition has developed for classification of the normal and abnormal lesion in CTLM images. The performance of the proposed CAD system has been measured with different metrics including accuracy, sensitivity, and specificity and area under receiver operative characteristics (AROC), which are 95.2, 92.4, 98.1, and 0.98%, respectively.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2026

Dark Energy Survey Year 6 Results: Photometric Dataset for Cosmology

We describe the photometric dataset assembled from the full 6 yr of observations by the Dark Energy Survey (DES) in support of static-sky cosmology analyses. DES Y6 Gold is a curated dataset derived from DES Data Release 2 (DR2) that incorporates improved measurement, photometric calibration, object classification and value-added information. Y6 Gold comprises nearly 5000 deg$^{2}$ of grizY imaging in the south Galactic cap and includes 669 million objects with a depth of i$_{AB}$ ∼ 23.4 mag at a signal-to-noise ratio ∼ 10 for extended objects and a top-of-the-atmosphere photometric uniformity <2 mmag. Y6 Gold augments DES DR2 with simultaneous fits to multiepoch photometry for more robust galaxy shapes, colors, and photometric redshift estimates. Y6 Gold features improved morphological star–galaxy classification with an efficiency of 98.6% and a contamination of 0.8% for galaxies with 17.5 < i$_{AB}$ < 22.5. Additionally, it includes per-object quality information, and accompanying maps of the footprint coverage, masked regions, imaging depth, survey conditions, and astrophysical foregrounds that are used for cosmology analyses. After quality selections, benchmark samples contain 448 million galaxies and 120 million stars. This publication is complemented by data access and documentation.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2015

Computer-aided detection of renal calculi from noncontrast CT images using TV-flow and MSER features

Purpose: Renal calculi are common extracolonic incidental findings on computed tomographic colonography (CTC). This work aims to develop a fully automated computer-aided diagnosis system to accurately detect renal calculi on CTC images. Methods: The authors developed a total variation (TV) flow method to reduce image noise within the kidneys while maintaining the characteristic appearance of renal calculi. Maximally stable extremal region (MSER) features were then calculated to robustly identify calculi candidates. Finally, the authors computed texture and shape features that were imported to support vector machines for calculus classification. The method was validated on a dataset of 192 patients and compared to a baseline approach that detects calculi by thresholding. The authors also compared their method with the detection approaches using anisotropic diffusion and nonsmoothing. Results: At a false positive rate of 8 per patient, the sensitivities of the new method and the baseline thresholding approach were 69% and 35% (p < 1e − 3) on all calculi from 1 to 433 mm{sup 3} in the testing dataset. The sensitivities of the detection methods using anisotropic diffusion and nonsmoothing were 36% and 0%, respectively. The sensitivity of the new method increased to 90% if only larger and more clinically relevant calculi were considered. Conclusions: Experimental results demonstrated that TV-flow and MSER features are efficient means to robustly and accurately detect renal calculi on low-dose, high noise CTC images. Thus, the proposed method can potentially improve diagnosis.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2018

Marine Animal Classification With Correntropy-Loss-Based Multiview Learning

To analyze marine animals' behavior, seasonal distribution, and abundance, digital imagery can be acquired by a camera or a Lidar. Depending on the quantity and properties of acquired imagery, the animals are characterized as either features (shape, color, texture, etc.) or dissimilarity matrices derived from different shape analysis methods (shape context, internal distance shape context, etc.). For both cases, multiview learning is critical in integrating more than one set of feature/dissimilarity matrix for higher classification accuracy. This paper adopts correntropy loss as the cost function in multiview learning, which has favorable statistical properties for rejecting noise. For the case of features, the correntropy-loss-based multiview learning and its “entrywise” variation are developed based on the multiview intact space learning algorithm. For the case of dissimilarity matrices, the robust Euclidean embedding algorithm is extended to its multiview form with the correntropy loss function. Results from simulated data and real-world marine animal imagery show that the proposed algorithms can effectively enhance classification rate as well as suppress noise under different noise conditions.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Structural and stellar-population properties versus bulge types in Sloan Digital Sky Survey central galaxies

ABSTRACT This paper studies pseudo-bulges (P-bulges) and classical bulges (C-bulges) in Sloan Digital Sky Survey (SDSS) central galaxies using the new bulge indicator ΔΣ1, which measures relative central stellar-mass surface density within 1 kpc. We compare ΔΣ1 to the established bulge-type indicator Δ〈μe〉 from Gadotti (2009) and show that classifying by ΔΣ1 agrees well with Δ〈μe〉. ΔΣ1 requires no bulge–disc decomposition and can be measured on SDSS images out to z = 0.07. Bulge types using it are mapped on to 20 different structural and stellar-population properties for 12 000 SDSS central galaxies with masses 10.0 < log M*/M⊙ < 10.4. New trends emerge from this large sample. Structural parameters show fairly linear log–log relations versus ΔΣ1 and Δ〈μe〉 with only moderate scatter, while stellar-population parameters show a highly non-linear ‘elbow’ in which specific star formation rate remains roughly flat with increasing central density and then falls rapidly at the elbow, where galaxies begin to quench. P-bulges occupy the low-density end of the horizontal arm of the elbow and are universally star forming, while C-bulges occupy the elbow and the vertical branch and exhibit a wide range of star formation rates at a fixed density. The non-linear relation between central density and star formation rate has been seen before, but this mapping on to bulge class is new. The wide range of star formation rates in C-bulges helps to explain why bulge classifications using different parameters have sometimes disagreed in the past. The elbow-shaped relation between density and stellar indices suggests that central structure and stellar populations evolve at different rates as galaxies begin to quench.

Luo, Yifei↗

DOE OSTI2016

SU-F-R-14: PET Based Radiomics to Predict Outcomes in Patients with Hodgkin Lymphoma

Purpose: To identify PET-based radiomics features associated with high refractory/relapsed disease risk for Hodgkin lymphoma patients. Methods: A total of 251 Hodgkin lymphoma patients including 19 primary refractory and 9 relapsed patients were investigated. All patients underwent an initial pre-treatment diagnostic FDG PET/CT scan. All cancerous lymph node regions (ROIs) were delineated by an experienced physician based on thresholding each volume of disease in the anatomical regions to SUV>2.5. We extracted 122 image features and evaluated the effect of ROI selection (the largest ROI, the ROI with highest mean SUV, merged ROI, and a single anatomic region [e.g. mediastinum]) on classification accuracy. Random forest was used as a classifier and ROC analysis was used to assess the relationship between selected features and patient’s outcome status. Results: Each patient had between 1 and 9 separate ROIs, with much intra-patient variability in PET features. The best model, which used features from a single anatomic region (the mediastinal ROI, only volumes>5cc: 169 patients with 12 primary refractory) had a classification accuracy of 80.5% for primary refractory disease. The top five features, based on Gini index, consist of shape features (max 3D-diameter and volume) and texture features (correlation and information measure of correlation1&2). In the ROC analysis, sensitivity and specificity of the best model were 0.92 and 0.80, respectively. The area under the ROC (AUC) and the accuracy were 0.86 and 0.86, respectively. The classification accuracy was less than 60% for other ROI models or when ROIs less than 5cc were included. Conclusion: This study showed that PET-based radiomics features from the mediastinal lymph region are associated with primary refractory disease and therefore may play an important role in predicting outcomes in Hodgkin lymphoma patients. These features could be additive beyond baseline tumor and clinical characteristics, and may warrant more aggressive treatment.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2017

An Ensemble Method for Classifying Regional Disease Patterns of Diffuse Interstitial Lung Disease Using HRCT Images from Different Vendors

We propose the use of ensemble classifiers to overcome inter-scanner variations in the differentiation of regional disease patterns in high-resolution computed tomography (HRCT) images of diffuse interstitial lung disease patients obtained from different scanners. A total of 600 rectangular 20 × 20-pixel regions of interest (ROIs) on HRCT images obtained from two different scanners (GE and Siemens) and the whole lung area of 92 HRCT images were classified as one of six regional pulmonary disease patterns by two expert radiologists. Textual and shape features were extracted from each ROI and the whole lung parenchyma. For automatic classification, individual and ensemble classifiers were trained and tested with the ROI dataset. We designed the following three experimental sets: an intra-scanner study in which the training and test sets were from the same scanner, an integrated scanner study in which the data from the two scanners were merged, and an inter-scanner study in which the training and test sets were acquired from different scanners. In the ROI-based classification, the ensemble classifiers showed better (p < 0.001) accuracy (89.73%, SD = 0.43) than the individual classifiers (88.38%, SD = 0.31) in the integrated scanner test. The ensemble classifiers also showed partial improvements in the intra- and inter-scanner tests. In the whole lung classification experiment, the quantification accuracies of the ensemble classifiers with integrated training (49.57%) were higher (p < 0.001) than the individual classifiers (48.19%). Furthermore, the ensemble classifiers also showed better performance in both the intra- and inter-scanner experiments. We concluded that the ensemble classifiers provide better performance when using integrated scanner images.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2019

Combining disdrometer, microscopic photography, and cloud radar to study distributions of hydrometeor types, size and fall velocity

Addressing solid precipitation poses additional challenges compared to warm rain due to complex hydrometeor shapes involved, including the dependence of fall velocity on hydrometeor sizes, hydrometeor size distributions, and hydrometeor classification. This study is an extension of our previous work (Niu et al., 2010) to address these challenges by combining measurements from a PARSIVEL disdrometer, microscope photography, and millimeter wavelength cloud radar. The combined measurements are analyzed to classify the precipitation hydrometeor types, examine the dependence of fall velocity on hydrometeor sizes for different hydrometeor types, and determine the best distributions to describe the hydrometeor size distributions of different hydrometeor types. The results show: (1) Hydrometeors can be classified to four main types of raindrop, graupel, snowflake and mixed-phase according to the dependence of terminal velocity on particle sizes, corresponding microscope photos and cloud radar observations; (2) There are significant scatters in fall velocity for a given hydrometer size velocities, and the fall velocity spread for the solid hydrometeors appear wider than that for raindrops across hydrometeor sizes, with that for the mixed-phase precipitation being largest, suggesting that the effects of hydrometeor shape on hydrometeor fall velocities; (3) Hydrometeor size distributions for the four types can all be well described by the Gamma or Weibull distribution; Weibull (Gamma) distribution performs better when skewness is less (larger) than 2.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Multi-Task Learning of Scanning Electron Microscopy and Synthetic Thermal Tomography Images for Detection of Defects in Additively Manufactured Metals

One of the key challenges in laser powder bed fusion (LPBF) additive manufacturing of metals is the appearance of microscopic pores in 3D-printed metallic structures. Quality control in LPBF can be accomplished with non-destructive imaging of the actual 3D-printed structures. Thermal tomography (TT) is a promising non-contact, non-destructive imaging method, which allows for the visualization of subsurface defects in arbitrary-sized metallic structures. However, because imaging is based on heat diffusion, TT images suffer from blurring, which increases with depth. We have been investigating the enhancement of TT imaging capability using machine learning. In this work, we introduce a novel multi-task learning (MTL) approach, which simultaneously performs the classification of synthetic TT images, and segmentation of experimental scanning electron microscopy (SEM) images. Synthetic TT images are obtained from computer simulations of metallic structures with subsurface elliptical-shaped defects, while experimental SEM images are obtained from imaging of LPBF-printed stainless-steel coupons. MTL network is implemented as a shared U-net encoder between the classification and the segmentation tasks. Results of this study show that the MTL network performs better in both the classification of synthetic TT images and the segmentation of SEM images tasks, as compared to the conventional approach when the individual tasks are performed independently of each other.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Background-Aware 3-D Point Cloud Segmentation With Dynamic Point Feature Aggregation

With the proliferation of LiDAR sensors and 3-D vision cameras, 3-D point cloud analysis has attracted significant attention in recent years. In this article, we propose a novel 3-D point cloud learning network, referred to as dynamic point feature aggregation network (DPFA-Net), by selectively performing the neighborhood feature aggregation (FA) with dynamic pooling and an attention mechanism. DPFA-Net has two variants for semantic segmentation and classification of 3-D point clouds. As the core module of the DPFA-Net, we propose an FA layer, in which features of the dynamic neighborhood of each point are aggregated via a self-attention mechanism. In contrast to other segmentation models, which aggregate features from fixed neighborhoods, our approach can aggregate features from different neighbors in different layers providing a more selective and broader view to the query points and focusing more on the relevant features in a local neighborhood. In addition, to further improve the performance of semantic segmentation, we exploit the background–foreground (BF) information and present two novel approaches, namely, two-stage BF-Net and BF regularization. Experimental results show that the proposed DPFA-Net achieves the state-of-the-art overall accuracy score of 89.22% for semantic segmentation on the Stanford large-scale 3-D Indoor Spaces (S3DIS) dataset and provides consistently satisfactory performance across different tasks of semantic segmentation, part segmentation, and 3-D object classification. Furthermore, our model achieves 93.1% accuracy on the ModelNet40 dataset and provides a mean shape intersection-over-union (IoU) value of 85.5% for part segmentation on the ShapeNet-Part dataset. It is a also computationally more efficient compared to other methods.

3-D↗