Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Cluster Analysis”

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 1,387 records · Page 77
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Development of an Alfalfa SNP Array and Its Use to Evaluate Patterns of Population Structure and Linkage Disequilibrium

A large set of genome-wide markers and a high-throughput genotyping platform can facilitate the genetic dissection of complex traits and accelerate molecular breeding applications. Previously, we identified about 0.9 million SNP markers by sequencing transcriptomes of 27 diverse alfalfa genotypes. From this SNP set, we developed an Illumina Infinium array containing 9,277 SNPs. Using this array, we genotyped 280 diverse alfalfa genotypes and several genotypes from related species. About 81% (7,476) of the SNPs met the criteria for quality control and showed polymorphisms. The alfalfa SNP array also showed a high level of transferability for several closely related Medicago species. Principal component analysis and model-based clustering showed clear population structure corresponding to subspecies and ploidy levels. Within cultivated tetraploid alfalfa, genotypes from dormant and nondormant cultivars were largely assigned to different clusters; genotypes from semidormant cultivars were split between the groups. The extent of linkage disequilibrium (LD) across all genotypes rapidly decayed to 26 Kbp at r 2 = 0.2, but the rate varied across ploidy levels and subspecies. A high level of consistency in LD was found between and within the two subpopulations of cultivated dormant and nondormant alfalfa suggesting that genome-wide association studies (GWAS) and genomic selection (GS) could be conducted using alfalfa genotypes from throughout the fall dormancy spectrum. However, the relatively low LD levels would require a large number of markers to fully saturate the genome.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2012

High Performance Network Monitoring [Slides]

Network Monitoring requires a substantial use of data and error analysis to overcome issues with clusters. Zenoss and Splunk help to monitor system log messages that are reporting issues about the clusters to monitoring services. Infiniband infrastructure on a number of clusters upgraded to ibmon2. ibmon2 requires different filters to report errors to system administrators. Focus for this summer is to: (1) Implement ibmon2 filters on monitoring boxes to report system errors to system administrators using Zenoss and Splunk; (2) Modify and improve scripts for monitoring and administrative usage; (3) Learn more about networks including services and maintenance for high performance computing systems; and (4) Gain a life experience working with professionals under real world situations. Filters were created to account for clusters running ibmon2 v1.0.0-1 10 Filters currently implemented for ibmon2 using Python. Filters look for threshold of port counters. Over certain counts, filters report errors to on-call system administrators and modifies grid to show local host with issue.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Facial Named Entity Recognition by Attention-Based Graph Convolutional Neural Network

In the realm of facial recognition and analysis, the ability to accurately cluster large datasets of facial images stands as a cornerstone for various applications, ranging from security surveillance to user biometric identification. This project evolves a novel approach to facial data clustering by embedding facial images into a high-dimensional vector space using an advanced embedding model trained on separate data and assumes a graph-like structure on the high-dimensional vectors. We find our method works significantly better than common shallow methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Bacterial Biomarkers of Marcellus Shale Activity in Pennsylvania

Unconventional oil and gas (UOG) extraction, also known as hydraulic fracturing, is becoming more prevalent with the increasing use and demand for natural gas; however, the full extent of its environmental impacts is still unknown. Here we measured physicochemical properties and bacterial community composition of sediment samples taken from twenty-eight streams within the Marcellus shale formation in northeastern Pennsylvania differentially impacted by hydraulic fracturing activities. Fourteen of the streams were classified as UOG+, and thirteen were classified as UOG- based on the presence of UOG extraction in their respective watersheds. One stream was located in a watershed that previously had UOG extraction activities but was recently abandoned. We utilized high-throughput sequencing of the 16S rRNA gene to infer differences in sediment aquatic bacterial community structure between UOG+ and UOG- streams, as well as correlate bacterial community structure to physicochemical water parameters. Although overall alpha and beta diversity differences were not observed, there were a plethora of significantly enriched operational taxonomic units (OTUs) within UOG+ and UOG- samples. Our biomarker analysis revealed many of the bacterial taxa enriched in UOG+ streams can live in saline conditions, such as Rubrobacteraceae. In addition, several bacterial taxa capable of hydrocarbon degradation were also enriched in UOG+ samples, including Oceanospirillaceae. Methanotrophic taxa, such as Methylococcales, were significantly enriched as well. Several taxa that were identified as enriched in these samples were enriched in samples taken from different streams in 2014; moreover, partial least squares discriminant analysis (PLS-DA) revealed clustering between streams from the different studies based on the presence of hydraulic fracturing along the second axis. This study revealed significant differences between bacterial assemblages within stream sediments of UOG+ and UOG- streams and identified several potential biomarkers for evaluating and monitoring the response of autochthonous bacterial communities to potential hydraulic fracturing impacts.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Description of Pegethrix niliensis sp. nov., a Novel Cyanobacterium from the Nile River Basin, Egypt: A Polyphasic Analysis and Comparative Study of Related Genera in the Oculatellales Order

In this paper, we examine the filamentous cyanobacterial strain NILCB16 and describe it as a new species within the genus Pegethrix. The original population was sampled from a mat growing in an irrigation canal in the Nile River, Egypt. Initially classified under Plectonema or Planktolyngbya, the strain is a potential producer of the toxins microcystin and β-N-Methylamino-L-Alanine (BMAA). Additionally, we reviewed the taxonomic relationships between the Oculatellales genera. To describe the new species, we conducted a polyphasic study, encompassing 16S rRNA gene phylogenetic analyses performed using both Maximum Likelihood and Bayesian methods, sequence identity (p-distance) analysis, 16S-23S ITS secondary structures, and morphological and habitat comparisons. The phylogenetic analysis revealed that strain NILCB16 clustered within the Pegethrix clade with strong phylogenetic support, but in a distinct position from other species in the genus. The strain shared a maximum 16S rRNA gene identity of 97.3% with P. qiandaoensis and 96.1% with the type species, P. bostrychoides. Morphologically, NILCB16 can be differentiated from other species in the genus by its lack of false branching. Our phylogenetic analyses also show that Pegethrix, Cartusia, Elainella, and Maricoleus are clustered with strong phylogenetic support. They exhibit high 16S rRNA gene identity and are morphologically indistinguishable, suggesting they could potentially be merged into a single genus in the future.

Hentschke, Guilherme Scotta (ORCID:000000034396024↗
DOE OSTI2017

High-resolution Spectroscopic Abundances of Red Giant Branch Stars in NGC 6681

We obtain high-resolution spectra of nine red giant branch stars in NGC 6681 and perform the first detailed abundance analysis of stars in this cluster. We confirm cluster membership for these stars based on consistent radial velocities of 214.5 ± 3.7 km s{sup −1} and find a mean [Fe/H] = −1.63 ± 0.07 dex and [ α /Fe] = 0.42 ± 0.11 dex. Additionally, we confirm the existence of a Na–O anti-correlation in NGC 6681 and identify two populations of stars with unique abundance trends. With the use of HST photometry from Sarajedini et al. and Piotto et al. we are able to identify these two populations as discrete sequences in the cluster CMD. Although we cannot confirm the nature of the polluter stars responsible for the abundance differences in these populations, these results do help put constraints on possible polluter candidates.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

RELICS: High-resolution Constraints on the Inner Mass Distribution of the z = 0.83 Merging Cluster RXJ0152.7-1357 from Strong Lensing

Strong gravitational lensing (SL) is a powerful means of mapping the distribution of dark matter. Here, we perform an SL analysis of the prominent X-ray cluster RXJ0152.7-1357 ( z = 0.83, also known as CL0152.7-1357) in Hubble Space Telescope images, taken in the framework of the Reionization Lensing Cluster Survey (RELICS). On top of a previously known z = 3.93 galaxy multiply imaged by RXJ0152.7-1357, for which we observe an additional multiple image, guided by a light-traces-mass approach, we identify seven new sets of multiply imaged background sources lensed by this cluster, spanning the redshift range [1.79–3.93]. A total of 25 multiple images are seen over a small area of ~0.4 arcmin 2 , enabling us to put relatively high-resolution constraints on the inner matter distribution. Although modestly massive, the high degree of substructure, together with its very elongated shape, makes RXJ0152.7-1357 a very efficient lens for its size. This cluster also comprises the third-largest sample of z ~ 6–7 candidates in the RELICS survey. Lastly, we present a comparison of our resulting mass distribution and magnification estimates with those from a Lenstool model. These models are made publicly available through the Mikulski Archive for Space Telescopes.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1972

Automatic classification of soils and vegetation with ERTS-1 data

Preliminary results of a test of a computerized analysis method using ERTS 1 data are presented. The method consisted of a four-spectral-band supervised, maximum likelihood, Gaussian classifier with training statistics derived through a combination of clustering and manual methods. The multivariate analysis method leads to the assignment of each resolution element of the data to one of a preselected set of discrete classes. The data frame was an area over the Texas-Oklahoma border including Lake Texoma. The study suggests that multispectral scanner data coupled with machine processing shows promise for earth surface cover surveys. Futhermore, the processing time is short and consequently the costs are low; a full frame can be analyzed completely within 48 hours.

Landgrebe, D. A.↗
NASA NTRS1976

AHIMSA - Ad hoc histogram information measure sensing algorithm for feature selection in the context of histogram inspired clustering techniques

An algorithm is proposed for dimensionality reduction in the context of clustering techniques based on histogram analysis. The approach is based on an evaluation of the hills and valleys in the unidimensional histograms along the different features and provides an economical means of assessing the significance of the features in a nonparametric unsupervised data environment. The method has relevance to remote sensing applications.

Dasarathy, B. V.↗
NASA NTRS1983

Ultraviolet interstellar absorption toward HD 5980 in the Small Magellanic Cloud

Individual high-dispersion International Ultraviolet Explorer (IUE) spectra of extragalactic stars are noisy because of the long exposure times required and the inherent limitations of the IUE detectors. A program has, therefore, been undertaken to obtain multiple ultraviolet spectra of the brightest Magellanic Cloud stars in order to produce composite spectra with higher signal-to-noise ratios than in individual spectra. The present investigation is concerned with results for HD 5980. The target star, HD 5980, is one of the brightest extragalactic far-ultraviolet sources in the sky. HD 5980 is located in the northeast quadrant of NGC 346, the largest H II region and OB star cluster in the SMC. The analysis of the interstellar line spectrum of HD 5980 provides a number of conclusions about the complex absorption characteristics of the interstellar gas toward the SMC.

Fitzpatrick, E. L.↗
NASA NTRS1993

Automatic definition of spectral units in the equatorial regions of Mars

ISM was the first instrument that acquired spectra of small areas (25x25 sq km) on a planetary surface. The data set consists mainly in images made up of about 3000 such pixels, each one corresponding to a near-infrared spectrum (0.76 to 3.15 microns in 128 spectral channels) with high signal-to-noise ratio. These data were used to define spectral units and to constrain the mineralogy of surface materials in the equatorial regions of the planet. Future spaceborne imaging spectrometers (e.g. OMEGA on board the Mars-94 Russian spacecraft) are expected to provide several hundred times as much data as ISM, so there's a strong need for fast and reliable processing methods. The present work is an attempt to define spectral units in the region of Syrtis Major-Isidis Planitia by means of G-mode analysis. The method allows to cluster the spectra according to their similarities; different levels of classification can be achieved by tuning a threshold of confidence.

Erard, Stephane↗
NASA NTRS1997

Does the Ultraviolet Imager on Polar Detect Cometesimals?

The Earth's dayglow images obtained by the Ultraviolet Imager (UVI) on Polar should show darkened pixels if cometesimals bombard Earth. This darkening occurs because the UV emissions are absorbed by water between the altitude where the dayglow emissions are produced and the Polar spacecraft. We have asked: (1) Does the UVI detect darkened pixels? (2) If so, what are their characteristics? (3) What can the UVI say about the presence/absence of cometesimals? The UVI does see dark pixels, which tend to be clustered. But the calibration data obtained from the same instrument just before the instrument was launched show similarly darkened pixel clusters. After a though statistical analysis of these images, we conclude that the darkened pixels are instrumental artifacts. Therefore, the UVI does not provide evidence for the existence of the cometesimals.

Parks, G. K.↗
NASA NTRS2018

Fire Induced Rock Spalls as Long-Term Traps for Ash

Severe fires accelerate rock weathering by spalling and exfoliation, creating abundant peels, flakes or spalls. In the following years, these spalls serve as physical traps which accommodate fine particles of dust, ash, organic matter, etc. We searched for traces of ash trapped under the spalls, after four major fires: 1989, 2005, 2010 and 2015 in Israel. Samples were collected beneath the spalls that formed on the rock outcrops, and in the immediate vicinity above and below them. Three laboratory analyses were performed: pH, EC and color. Five mineral/organic compounds were measured across the Mid-Infrared (MIR) spectral region at diffuse reflectance infrared Fourier transform mode: Hydroxylapatite (HAp), charcoal, organic carbon, montmorillonite and kaolinite. Several statistical analyses were performed: MANOVA, PCA and silhouette analysis on K-means clustering. The results show evidence of ash trapped under the spalls formed during the 2005 and 2010 fires, 6 to 11 years after the fires. Charcoal presence is evident, as well as increased amounts of HAp and organic carbon. In the exposed soil above or below the burned rock outcrop, these values are lower. Negligible amounts of ash were measured 27 years after the fire. In the 2015 burned outcrop, large amounts of charcoal were found above and below the outcrop, but not under the spalls. It seems that on the carbonate slopes of Israel and under Mediterranean climate, the time required for spalls to begin functioning as traps is longer than one rainy season, while ash traces are preserved in these traps for a period of two-three decades.

ash↗
NASA NTRSDate not supplied

Using Machine Learning to Infer Material Properties of Debris Fragments from X-ray Images in the DebriSat Project

The DebriSat project is a collaboration effort with the NASA Orbital Debris Program Office, the U.S. Space Force Space Systems Command Center, The Aerospace Corporation, and the University of Florida. To date, over 200,000 fragments from this ground-based, hypervelocity impact experiment have been collected, and processing is underway to determine their physical characteristics, such as material, shape, color, characteristic length, and average cross-sectional area. The x-ray process is primarily used to identify the location of the fragments and estimated size for extraction, so that these physical characteristics can be assessed. This paper proposes a machine learning-based approach to characterize materials from x-ray images of debris fragments embedded in soft-catch foam used in the DebriSat project. The novel methodology discussed in this paper will highlight the use of x-ray imagery data to characterize these fragments without extraction or a human-in-the-loop. Both supervised and unsupervised machine learning techniques are utilized with this approach to infer the physical parameters of the fragments embedded in the soft-catch foam panels used in the impact experiment based on x-ray images of the foam panels. Additionally, 3D reconstructions of the extracted fragments are created with images taken from two different angles using the structure from motion (SfM) method. The characteristic lengths and shape from the 3D reconstruction, alongside the physical characteristics of the debris, are used in the inference of the material type. To develop and test the approach, a dataset of x-ray images of debris fragments of varying sizes and materials is collected. Supervised learning methods such as convolutional neural networks (CNNs), support vector machines (SVM), decision trees, and random forest classifiers are used due to the high-dimensional feature spaces of the debris and nonlinear decision boundaries for material categorization. Given the limited pre-labeled data of embedded debris materials smaller than 10 mm, unsupervised machine learning techniques such as clustering algorithms and autoencoders are used, in addition to supervised learning methods. The clustering algorithms group similar fragments together based on their physical properties, and autoencoders reduce the dimensionality of the x ray images and extract relevant features. The performance of the proposed approach's is analyzed using a range of statistical methods, including confusion matrices, receiver operating characteristic curves, and precision-recall curves. The results are compared with those obtained using a baseline approach that relies on manual identification and classification of debris fragments. To evaluate the effectiveness of different machine learning methods, statistical tests such as t-tests, ANOVA, and cross-validation are performed, comparing the performance of CNNs, SVMs, clustering algorithms, and autoencoders. Additional analysis needs to be conducted to identify any sources of bias or variability that may affect the results, such as variations in imaging conditions or fragmentation patterns. Other topics explored are limitations, refinements, and the potential use of semi-supervised learning techniques, such as self-training to label unlabeled datasets and co-training using x-ray images taken from two different angles as two different models.

Saik Anam Siam↗
NASA NTRS2023

Using Machine Learning to Infer Material Properties of Debris Fragments from X-ray Images in the DebriSat Project

The DebriSat project is a collaboration effort with the NASA Orbital Debris Program Office, the U.S. Space Force Space Systems Command Center, The Aerospace Corporation, and the University of Florida. To date, over 200,000 fragments from this ground-based, hypervelocity impact experiment have been collected, and processing is underway to determine their physical characteristics, such as material, shape, color, characteristic length, and average cross-sectional area. The x-ray process is primarily used to identify the location of the fragments and estimated size for extraction, so that these physical characteristics can be assessed. This paper proposes a machine learning-based approach to characterize materials from x-ray images of debris fragments embedded in soft-catch foam used in the DebriSat project. The novel methodology discussed in this paper will highlight the use of x-ray imagery data to characterize these fragments without extraction or a human-in-the-loop. Both supervised and unsupervised machine learning techniques are utilized with this approach to infer the physical parameters of the fragments embedded in the soft-catch foam panels used in the impact experiment based on x-ray images of the foam panels. Additionally, 3D reconstructions of the extracted fragments are created with images taken from two different angles using the structure from motion (SfM) method. The characteristic lengths and shape from the 3D reconstruction, alongside the physical characteristics of the debris, are used in the inference of the material type. To develop and test the approach, a dataset of x-ray images of debris fragments of varying sizes and materials is collected. Supervised learning methods such as convolutional neural networks (CNNs), support vector machines (SVM), decision trees, and random forest classifiers are used due to the high-dimensional feature spaces of the debris and nonlinear decision boundaries for material categorization. Given the limited pre-labeled data of embedded debris materials smaller than 10 mm, unsupervised machine learning techniques such as clustering algorithms and autoencoders are used, in addition to supervised learning methods. The clustering algorithms group similar fragments together based on their physical properties, and autoencoders reduce the dimensionality of the x ray images and extract relevant features. The performance of the proposed approach's is analyzed using a range of statistical methods, including confusion matrices, receiver operating characteristic curves, and precision-recall curves. The results are compared with those obtained using a baseline approach that relies on manual identification and classification of debris fragments. To evaluate the effectiveness of different machine learning methods, statistical tests such as t-tests, ANOVA, and cross-validation are performed, comparing the performance of CNNs, SVMs, clustering algorithms, and autoencoders. Additional analysis needs to be conducted to identify any sources of bias or variability that may affect the results, such as variations in imaging conditions or fragmentation patterns. Other topics explored are limitations, refinements, and the potential use of semi-supervised learning techniques, such as self-training to label unlabeled datasets and co-training using x-ray images taken from two different angles as two different models.

Saik Anam Siam↗
DOE OSTI2019

Identification of Critical Airports from the Perspective of Delay and Disruption Propagation in Air Travel Networks

It is well-known that delays and disruptions that originate at certain airports end up spreading spread on the network to other airports, sometimes straining a large fraction of the system. Hence data-driven modeling of this phenomenon is important from the standpoint of planning and strategy. In this work, utilizing the publicly available data from the Federal Aviation Administration (FAA), we employ the well-known influence maximization paradigm, to mine for the top airports in the US airport network, from the perspective of delay spread. In the process, we develop a diffusion simulator and implement a greedy algorithm to aid our task. We then explore the temporal trends by considering one winter month and one summer month in our analysis. Finally, we characterize the clusters in the US airport network and investigate the patterns of delay spread when airports in each of the clusters are affected.

Visweswara Sathanur, Arun↗
DOE OSTI2013

Analysis of physics-based preconditioning for single-phase subchannel equations

The (single-phase) subchannel approximations are used throughout nuclear engineering to provide an efficient flow simulation because the computational burden is much smaller than for computational fluid dynamics (CFD) simulations, and empirical relations have been developed and validated to provide accurate solutions in appropriate flow regimes. Here, the subchannel equations have been recast in a residual form suitable for a multi-physics framework. The Eigen spectrum of the Jacobian matrix, along with several potential physics-based preconditioning approaches, are evaluated, and the the potential for improved convergence from preconditioning is assessed. The physics-based preconditioner options include several forms of reduced equations that decouple the subchannels by neglecting crossflow, conduction, and/or both turbulent momentum and energy exchange between subchannels. Eigen-scopy analysis shows that preconditioning moves clusters of eigenvalues away from zero and toward one. A test problem is run with and without preconditioning. Without preconditioning, the solution failed to converge using GMRES, but application of any of the preconditioners allowed the solution to converge. (authors)

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Exploring Geothermal Potential of Great Basin Sub-Regions: Preprint

The INnovative Geothermal Exploration through Novel Investigations Of Undiscovered Systems (INGENIOUS) project aims to discover new, economically viable hidden geothermal systems in the Great Basin region by building on previous work in play fairway analysis and machine learning. A key objective of this project is to develop an exploration workflow to reduce geothermal exploration risks for hidden geothermal systems. A single preliminary play fairway workflow was developed from the assessment of the regional INGENIOUS geological, geophysical, and geochemical datasets. This workflow provided new preliminary predictive geothermal fairway maps for the INGENIOUS study area, which encompasses most of Nevada, western Utah, southern Idaho, southeastern Oregon, and easternmost California. However, a recent study (incorporating machine learning techniques) of a portion of Nevada identified four geologic domains and determined that the relative importance of individual datasets or features as indicators of geothermal potential may differ across these domains. The INGENIOUS study area includes a much larger and more geologically diverse region; therefore, additional geologic domains or sub-regions are expected. To assess the sub-regions in the INGENIOUS study area, principal component analysis and k-means clustering were applied. Preliminary results indicate that the INGENIOUS regional data cluster into groups that relate to different geologic domains in the Great Basin region. These include domains such as the Walker Lane, extensional western Great Basin region, broad lower strain region in the eastern Great Basin of western Utah and eastern Nevada, Quaternary volcanic fields, and the area adjacent to the Snake River Plain. These clusters are assessed to determine the key geologic drivers of the identified clusters. Understanding this variability can provide key insights for the exploration and characterization of hidden geothermal systems in the Great Basin region and could indicate the need to develop multiple geothermal conceptual models and play fairway workflows for the INGENIOUS study area.

GEOTHERMAL ENERGY↗