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At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

Feature Acquisition with Imbalanced Training Data

This work considers cost-sensitive feature acquisition that attempts to classify a candidate datapoint from incomplete information. In this task, an agent acquires features of the datapoint using one or more costly diagnostic tests, and eventually ascribes a classification label. A cost function describes both the penalties for feature acquisition, as well as misclassification errors. A common solution is a Cost Sensitive Decision Tree (CSDT), a branching sequence of tests with features acquired at interior decision points and class assignment at the leaves. CSDT's can incorporate a wide range of diagnostic tests and can reflect arbitrary cost structures. They are particularly useful for online applications due to their low computational overhead. In this innovation, CSDT's are applied to cost-sensitive feature acquisition where the goal is to recognize very rare or unique phenomena in real time. Example applications from this domain include four areas. In stream processing, one seeks unique events in a real time data stream that is too large to store. In fault protection, a system must adapt quickly to react to anticipated errors by triggering repair activities or follow- up diagnostics. With real-time sensor networks, one seeks to classify unique, new events as they occur. With observational sciences, a new generation of instrumentation seeks unique events through online analysis of large observational datasets. This work presents a solution based on transfer learning principles that permits principled CSDT learning while exploiting any prior knowledge of the designer to correct both between-class and withinclass imbalance. Training examples are adaptively reweighted based on a decomposition of the data attributes. The result is a new, nonparametric representation that matches the anticipated attribute distribution for the target events.

Thompson, David R.↗
NASA NTRS2011

Nakhla: a Martian Meteorite with Indigenous Organic Carbonaceous Features

The Nakhla meteorite possesses discrete, well defined, structurally coherent morphologies of carbonaceous phases present within iddingsite alteration zones. Based upon both isotopic measurements and analysis of organic phases the presence of pre-terrestrial organics is now recognized. Within the microcrystalline layers of Nakhla s iddingsite, discrete clusters of salt crystals are present. These salts are predominantly halite (NaCl) with minor MgCl2 crystals. Some CaSO4, likely gypsum, appears to be partially intergrown with some of the halite. EDX mapping shows discrete C-rich features are interspersed among these crystals. A hollow semi-spherical bowl structure (~ 3 m ) has been identified and analyzed after using a focused ion beam (FIB) to cut a transverse TEM thin section of the feature and the underlying iddingsite. TEM/EDX analysis reveals that the feature is primarily carbonaceous containing C with lesser amounts of Si, S, Ca, Cl, F, Na, and minor Mn and Fe; additionally a small peak consistent with N, which has been previously seen in Nakhla carbonaceous matter, is also present. Selected area electron diffraction (SAED) shows that this C-rich material is amorphous (lacking any long-range crystallographic order) and is not graphite or carbonate. Micro-Raman spectra acquired from the same surface from which the FIB section was extracted demonstrate a typical kerogen-like D and G band structure with a weak absorption peak at 1350 and a stronger peak at 1600/cm. The C-rich feature is intimately associated with both the surrounding halite and underlying iddingsite matrix. Both iddingsite and salts are interpreted as having formed as evaporate assemblages from progressive evaporation of water bodies on Mars. This assemblage, sans the carbonaceous moieties, closely resembles iddingsite alteration features previously described which were interpreted as indigenous Martian assemblages. These distinctive macromolecular carbonaceous structures in Nakhla may represent one of the sources of the high molecular weight organic material previously identified in Nakhla. While we do not speculate on the origin of these unique carbonaceous structures, we note that the significance of such observations is that it may allow us to construct a C-cycle for Mars based on the C chemistry of the Martian meteorites with obvious implications for astrobiology and the prebiotic evolution of Mars. In any case, our observations strongly suggest that organic C exists as micrometersize, discrete structures on Mars.

McKay, D. S.↗
NASA NTRS2012

Thermal Behaviour of Unusual Local-Scale Surface Features on Vesta

On Vesta, the region of the infrared spectrum beyond approximately 3.5 micrometers is dominated by the thermal emission of the asteroid's surface, which can be used to determine surface temperature by means of temperature-retrieval algorithms. The thermal behavior of areas of unusual albedo seen at the local scale can be related to physical properties that can provide information about the origin of those materials. Dawn's Visible and Infrared Mapping Spectrometer (VIR) hyperspectral cubes are used to retrieve surface temperatures, with high accuracy as long as temperatures are greater than 180 K. Data acquired in the Survey phase (23 July through 29 August 2011) show several unusual surface features: 1) high-albedo (bright) and low-albedo (dark) material deposits, 2) spectrally distinct ejecta, 3) regions suggesting finer-grained materials. Some of the unusual dark and bright features were re-observed by VIR in the subsequent High-Altitude Mapping Orbit (HAMO) and Low-Altitude Mapping Orbit (LAMO) phases at increased pixel resolution. To calculate surface temperatures, we applied a Bayesian approach to nonlinear inversion based on the Kirchhoff law and the Planck function. These results were cross-checked through application of alternative methods. Here we present temperature maps of several local-scale features that were observed by Dawn under different illumination conditions and different local solar times. Some bright terrains have an overall albedo in the visible as much as 40% brighter than surrounding areas. Data from the IR channel of VIR show that bright regions generally correspond to regions with lower thermal emission, i.e. lower temperature, while dark regions correspond to areas with higher thermal emission, i.e. higher temperature. This behavior confirms that many of the dark appearances in the VIS mainly reflect albedo variations. In particular, it is shown that during maximum daily insolation, dark features in the equatorial region may rise to temperatures greater than 270 K. However, individual features may show different thermal behaviours, as a result of differences in composition and/or structure (e.g, average grain size of the surface regolith, porosity, etc.). To complement the temperature and near-infrared emissivity derived from the infrared spectra, a separate work is devoted to calculate thermal inertia and other thermal properties using theoretical models which solve the heat equation for airless bodies, and model the distribution of temperatures due to surface roughness variations.

Tosi, F.↗
NASA NTRS2013

Constraining Cometary Crystal Shapes from IR Spectral Features

A major challenge in deriving the silicate mineralogy of comets is ascertaining how the anisotropic nature of forsterite crystals affects the spectral features' wavelength, relative intensity, and asymmetry. Forsterite features are identified in cometary comae near 10, 11.05-11.2, 16, 19, 23.5, 27.5 and 33 microns [1-10], so accurate models for forsterite's absorption efficiency (Qabs) are a primary requirement to compute IR spectral energy distributions (SEDs, lambdaF lambda vs. lambda) and constrain the silicate mineralogy of comets. Forsterite is an anisotropic crystal, with three crystallographic axes with distinct indices of refraction for the a-, b-, and c-axis. The shape of a forsterite crystal significantly affects its spectral features [13-16]. We need models that account for crystal shape. The IR absorption efficiencies of forsterite are computed using the discrete dipole approximation (DDA) code DDSCAT [11,12]. Starting from a fiducial crystal shape of a cube, we systematically elongate/reduce one of the crystallographic axes. Also, we elongate/reduce one axis while the lengths of the other two axes are slightly asymmetric (0.8:1.2). The most significant grain shape characteristic that affects the crystalline spectral features is the relative lengths of the crystallographic axes. The second significant grain shape characteristic is breaking the symmetry of all three axes [17]. Synthetic spectral energy distributions using seven crystal shape classes [17] are fit to the observed SED of comet C/1995 O1 (Hale-Bopp). The Hale-Bopp crystalline residual better matches equant, b-platelets, c-platelets, and b-columns spectral shape classes, while a-platelets, a-columns and c-columns worsen the spectral fits. Forsterite condensation and partial evaporation experiments demonstrate that environmental temperature and grain shape are connected [18-20]. Thus, grain shape is a potential probe for protoplanetary disk temperatures where the cometary crystalline forsterite formed. The forsterite crystal shapes (equant, b-platelets, c-platelets, b-columns - excluding a- and c-columns) derived from our modeling [17] of comet Hale- Bopp, compared to laboratory synthesis experiments [18], suggests that these crystals are high temperature condensates. By observing and modeling the crystalline features in comet ISON, we may constrain forsterite crystal shape(s) and link to their formation temperature(s) and environment(s).

anisotropic crystal↗
NASA NTRS2017

Neptune Long-Lived Atmospheric Features in 2013 - 2015 from Small (28-cm) to Large (10-m) Telescopes

Since 2013, observations of Neptune with small telescopes (28-50 cm) have resulted in several detections of long-lived bright atmospheric features that have also been observed by large telescopes such as Keck II or Hubble. The combination of both types of images allows the study of the long-term evolution of major cloud systems in the planet. In 2013 and 2014 two bright features were present on the planet at southern mid-latitudes. These may have merged in late 2014, possibly leading to the formation of a single bright feature observed during 2015 at the same latitude. This cloud system was first observed in January 2015 and nearly continuously from July to December 2015 in observations with telescopes in the 2-10-m class and in images from amateur astronomers. These images show the bright spot as a compact feature at −40.1 +/- 1.6 deg planetographic latitude well resolved from a nearby bright zonal band that extended from −42 deg to −20 deg. The size of this system depends on wavelength and varies from a longitudinal extension of 8000 +/- 900 km and latitudinal extension of 6500 +/- 900 km in Keck II images in H and Ks bands to 5100 +/- 1400 km in longitude and 4500 +/- 1400 km in latitude in HST images in 657 nm. Over July to September 2015 the structure drifted westward in longitude at a rate of 24.48 +/- 0.03 deg/day or −94 +/- 3 m/s. This is about 30 m/s slower than the zonal winds measured at the time of the Voyager 2 flyby. Tracking its motion from July to November 2015 suggests a longitudinal oscillation of 16 deg in amplitude with a 90-day period, typical of dark spots on Neptune and similar to the Great Red Spot oscillation in Jupiter. The limited time covered by high-resolution observations only covers one full oscillation and other interpretations of the changing motions could be possible. HST images in September 2015 show the presence of a dark spot at short wavelengths located in the southern flank (planetographic latitude −47.0 deg) of the bright compact cloud observed throughout 2015. The drift rate of the bright cloud and dark spot translates to a zonal speed of −87.0 +/- 2.0 m/s, which matches the Voyager 2 zonal speeds at the latitude of the dark spot. Identification of a few other features in 2015 enabled the extraction of some limited wind information over this period. This work demonstrates the need of frequently monitoring Neptune to understand its atmospheric dynamics and shows excellent opportunities for professional and amateur collaborations.

dynamics↗
NASA NTRS2014

Titan Aerosol Analog Absorption Features Produced from Aromatics in the Far Infrared

We present results on the formation of Titan aerosol analogs produced via far-UV irradiation of five aromatic precursors: benzene, naphthalene, pyridine, quinoline and isoquinoline. This is the first reported evidence of far-IR emission features observed below 200 per cm in laboratory-created Titan aerosols. These laboratory studies were motivated by recent analyses of Cassini Composite Infrared Spectrometer (CIRS) spectra that show a broad aerosol emission feature in the far-IR spectral region centered near 140 per cm, which is unique to Titan's photochemically-produced aerosol. We find that all three of the aerosol analogs formed from nitrogen-containing aromatics have similar broad emission features near that of the observed CIRS far-IR aerosol spectral feature. In addition, the inclusion of 1.5% methane to that of trace amounts of benzene also gives rise to an aerosol with a weak far-IR emission feature located below 200 per cm.

atmosphere↗
NASA NTRS2018

Observations of Fast-Moving Features in the Debris Disk of AU Mic on a Three-Year Timescale: Confirmation and New Discoveries

Context. The nearby and young M star AU Mic is surrounded by a debris disk in which we previously identified a series of large-scale arch-like structures that have never been seen before in any other debris disk and that move outward at high velocities. Aims. We initiated a monitoring program with the following objectives: (1) track the location of the structures and better constrain their projected speeds, (2) search for new features emerging closer in, and ultimately (3) understand the mechanism responsible for the motion and production of the disk features. Methods. AU Mic was observed at 11 different epochs between August 2014 and October 2017 with the IR camera and spectrograph of SPHERE. These high-contrast imaging data were processed with a variety of angular, spectral, and polarimetric differential imaging techniques to reveal the faintest structures in the disk. We measured the projected separations of the features in a systematic way for all epochs. We also applied the very same measurements to older observations from the Hubble Space Telescope (HST) with the visible cameras STIS and ACS. Results. The main outcomes of this work are (1) the recovery of the five southeastern broad arch-like structures we identified in our first study, and confirmation of their fast motion (projected speed in the range 4–12 km/s); (2) the confirmation that the very first structures observed in 2004 with ACS are indeed connected to those observed later with STIS and now SPHERE; (3) the discovery of two new very compact structures at the northwest side of the disk (at 0.40′′ and 0.55′′ in May 2015) that move to the southeast at low speed; and (4) the identification of a new arch-like structure that might be emerging at the southeast side at about 0.4′′ from the star (as of May 2016). Conclusions. Although the exquisite sensitivity of SPHERE allows one to follow the evolution not only of the projected separation, but also of the specific morphology of each individual feature, it remains difficult to distinguish between possible dynamical scenarios that may explain the observations. Understanding the exact origin of these features, the way they are generated, and their evolution over time is certainly a significant challenge in the context of planetary system formation around M stars.

Boccaletti, A.↗
NASA NTRSDate not supplied

Assessing the Effects of Various Surface Textures and Features on Turbulent Heat Transfer in Hypersonic Flight

Experiments were conducted in the NASA Ames Hypervelocity Free Flight Aerodynamic Facility (ballistic range) to quantify the effects on turbulent convective heat transfer of distributed surface roughness, and of isolated features, representative of thermal protection systems on atmospheric entry vehicles. The surface textures and features were applied on the conic frusta of 45o sphere-cone models having a nose-to-base radius ratio of 0.5, similar to the forebody geometry of the Galileo and Pioneer-Venus entry probes. Test conditions were selected to provide turbulent roughness Reynolds numbers, k+, in the ranges expected for outer planet entry missions. Turbulent flow on the conic frustum was achieved by tripping the flow on the sphere-segment nose cap with distributed surface roughness, created by sand-blasting the nose cap. Surface textures included distributed, acreage, roughness, as well as cavity and groove discrete features. The majority of the data to be presented are results for distributed roughness, which includes both random, sand-grain-like roughness, and regular pattern roughness. The pattern roughness was designed to represent the roughness on woven thermal protection system materials, such as NASA’s 3-D Medium Density Carbon Phenolic (3MDCP), also known as HEEET, developed by the Heatshield for Extreme Entry Environments Technology project. The pattern roughness tested is a 3-D wavy surface, and includes three different roughness element height-to-spacing ratios representative of two configurations of 3MDCP, and spanning ratios measured both before, and after, ablation in an arc jet test facility. The patterns tested in the ballistic range were laser-etched on metal models, and represented an idealized version of the real-world materials in which each roughness element was nearly identical. Additional tests were performed wherein the laser-etched patterns were degraded by sand-blasting with various sized grit media, to produce regular patterns with superimposed irregular roughness, more representative of flight materials. Results of each will be compared. The discrete features tested included cylindrical cavities and rectangular grooves of various width-to-depth ratios. Cavities represent either heatshield damage, such as from micro-meteoroid and orbital debris (MMOD) damage, or from designed penetrations, such as on the Genesis sample return capsule. The grooves were scaled representations of seams between segments of HEEET material in a notional tiled thermal protection system. The tests examined the effects on turbulent heating downstream of the isolated features. The tests were conducted at speeds between 3 km/s and 4 km/s in air between 0.15 atm and 0.25 atm (Mach numbers between 9 and 12). Roughness Reynolds numbers, k+, ranged from 12 to 70 for the sand roughness, and as high as 200 for the pattern roughness. Boundary-layer parameters required for calculating k+ were evaluated using computational fluid dynamics simulations using the DPLR (Data Parallel Line Relaxation) code. Each model included both rough- and smooth-wall segments, and heat transfer augmentation factors were determined as the ratio of the rough-wall to smooth-wall heat flux measured on each test.

hypersonic↗
NASA NTRSDate not supplied

On A Higher Order Method for Anonymous Feature Processing

Some feature-driven navigation sources, such as cameras or lidars, often require measurement-to-feature associations between the collected data and an onboard feature catalog to be performed upstream of the filter. Standard navigation practice suggests the use of Kalman updates with measurements that have first passed residual editing tests, but this is often insufficient to prevent updates based upon incorrectly associated data, leading to filter degradation and divergence. Recent work has developed the anonymous feature processing (AFP) technique that eliminates reliance upon explicit feature associations outside of the filter entirely while maintaining desirable estimation performance. This paper continues by exploring the approximation employed by AFP, and a higher order approximation is presented to further improve the estimation performance of the AFP update.

James S Mccabe↗
NASA NTRSDate not supplied

Exploring Lightning and Convective Processes Using the Ground-Radar Multiplatform Precipitation Feature Database

The Multiplatform Precipitation Feature (MPF) database synthesizes coincident spaceborne and ground-based lightning and radar data in a framework of storm-based features, fusing broader spaceborne detection capabilities with process-based, storm-level analysis practices. The MPF database was designed to extend the scale and scope of investigations into the complex connections between precipitation, updrafts, and lightning. The NASA International Space Station Lightning Imaging Sensor (ISS LIS) serves as the source of lightning information for the database. The first iteration of the MPF database leveraged the NASA Global Precipitation Measurement (GPM) mission spaceborne Dual-frequency Precipitation Radar (DPR) to define features, along with contributions of microphysics data and vertical wind retrievals from the GPM Validation Network (VN) of ground-based radar data. The dependency on coincident ISS and GPM satellite overpasses of radars in a dual-Doppler configuration significantly limited the size of the initial database of features, referred to as VNMPFs, but established the database infrastructure and feasibility. We present here a second iteration of the MPF database that omits the GPM DPR and VN, instead incorporating data directly from selected proximal installations of the operational Next Generation Radar (NEXRAD) network that facilitate vertical wind retrievals via dual-Doppler analysis. These features based exclusively on ground-based polarimetric Doppler radar are hereafter referred to as Ground-Radar MPFs (GRMPFs). Removing the restriction of a coincident GPM overpass appreciably increases the size of the GRMPF database while incorporating more detailed information from higher-resolution radar data and retrievals. This expansion allows for unprecedented broad, robust statistical analyses of the electrical, kinematic, and microphysical characteristics of deep convective processes. These results highlight the potential for advancements in lightning meteorology made possible by combining multiple perspectives from global lightning measurements and ground-based radar data.

Lightning↗
NASA NTRS2024

Interpretable Machine Learning for Molecular Biosignatures: a Novel Single-Sample Feature Importance Method That Is Sensitive To Statistical Interactions

Isotope ratio mass spectrometry (IRMS) of volatiles (e.g., CO 2 ) promises to be a powerful tool for potential biosignature detection for future missions to ocean worlds (OW) such as Europa and Enceladus. Machine learning (ML) methods for IRMS data could enable science autonomy by onboard prediction of seawater chemistry and biosignature presence. However, ML models are likely to be complex and involve statistical interactions between features (variables), which can make predictions seem opaque and enigmatic. For ML predictions as significant as extraterrestrial biosignatures, we must place extraordinary confidence in models. It is therefore essential that these models make interpretable predictions (i.e., human-understandable) and include false-prediction diagnostics. We achieve high accuracy and interpretability in ML biosignature and seawater chemistry models for OW through a nearest-neighbors feature selection tool that detects statistical interactions between predictors, constructs interaction networks for visualization of selected features working together to make a prediction, and reports single-sample feature importance scores for false-detection diagnostics. Here we develop a novel single-sample nearest-neighbors projected distance regression(ssNPDR) feature selection method that improves upon existing single-sample algorithms through the inclusion of statistical interactions while providing false-prediction diagnostics for ML models.

geochemistry↗
DOE OSTI2021

Turbomachine component with coating-capturing feature for thermal insulation

The disclosure relates to turbomachine components which include one or more coating-capturing features for thermal insulation. A turbomachine component may include: a body having an exterior surface positioned within a hot gas path (HGP) section of a turbomachine; and a coating-capturing feature mounted on the exterior surface of the body and in thermal communication with the HGP section of the turbomachine, wherein the coating-capturing feature comprises: a first member positioned on the exterior surface of the body, the first member having at least one outer sidewall defining a first perimeter of the coating-capturing feature, a second member positioned on the first member and having at least one outer sidewall defining a second perimeter of the coating-capturing feature, wherein the first member separates the second member from the exterior surface of the body, and an indentation positioned between the first and second members.

Berry, Jonathan Dwight↗
DOE OSTI2022

Minimum feature size control in level set topology optimization via density fields

A level set topology optimization approach that uses an auxiliary density field to nucleate holes during the optimization process and achieves minimum feature size control in optimized designs is explored. The level set field determines the solid-void interface and the density field describes the distribution of a fictitious porous material using the solid isotropic material with penalization. These fields are governed by two sets of independent optimization variables which are initially coupled using a penalty for hole nucleation. The strength of the density field penalization and projection is gradually increased during the optimization process to promote a 0-1 density distribution. In addition, a second penalty regulates the evolution of the density field in the void phase. The treatment of the density field combined with the second penalty mitigate the appearance of small design features. The minimum feature size of optimized designs is controlled by the radius of the linear filter applied to the density optimization variables. The structural response is predicted by the extended finite element method, the sensitivities by the adjoint method, and the optimization variables are updated by a gradient-based optimization algorithm. Numerical examples investigate the robustness of this approach with respect to algorithmic parameters and mesh refinement. The results show the applicability of the combined density level set topology optimization approach for both optimal hole nucleation and for minimum feature size control in 2D and 3D. This comes, however, at the cost of a more complex problem formulation and additional computational cost due to an increased number of optimization variables.

42 ENGINEERING↗
DOE OSTI2021

Short-term apartment-level load forecasting using a modified neural network with selected auto-regressive features

Residential electricity load profiles and their diversity have become increasingly important to realize the benefits of Smart or Transactive Energy Networks (TENs). An important element of TENs will be practical, accurate, and implementable residential load forecasting techniques. While there have been many approaches to short-term load forecasting, few have included forecasting for individual households, partly because the high volatility and idiosyncrasies present in individual household load data can pose significant challenges. In this study, we develop a Convolutional Long Short-Term Memory-based neural network with Selected Autoregressive Features (termed a CLSAF model) to improve short-term household electricity load forecasting accuracy by employing three strategies: autoregressive features selection, exogenous features selection, and a “default” state to avoid overfitting at times of high load volatility. We include aggregations of apartments to floor and building level, because utilities may favor transactive approaches that rely on aggregator models, e.g., a cluster of consumers as opposed to an individual. We demonstrate that the CLSAF model, by virtue of its enhanced feature representation and modest computational resources, can accomplish load forecasting in a multi-family residential building across three spatial granularities (individual apartment/household, floor, and building levels), with an accuracy improvement of up to 25% compared to a persistence model. We propose a data screening technique to characterize time-series electricity-load data. This technique is suitable for integration into a TEN ecosystem and allows one to estimate confidence levels of the load forecasts to optimize computational resources and the risks associated with uncertain forecasts.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Understanding structure-processing relationships in metal additive manufacturing via featurization of microstructural images

Understanding and predicting accurate property-structure-processing relationships for additively manufactured components is important for both forward and inverse design of robust, reliable parts and assemblies. While direct mapping of process parameters to properties is sometimes plausible, it is often rendered difficult due to poor microstructural control. Exploring the direct relationship between processing conditions and microstructural features can thus provide significant physical insights and aid the overall design process. Here, in this study, we develop an automated high-throughput framework to simulate an uncertainty-aware additive manufacturing (AM) process, characterize microstructural images, and extract meaningful features/descriptors. A kinetic Monte Carlo (KMC) based model of the AM process is used to simulate microstructural evolution for a diverse set of experimentally relevant processing conditions. We perform a parametric study to explore the relationship between microstructural features and processing conditions. Our results indicate that a many-to-one mapping can exist between processing conditions and typical descriptors; therefore, multiple descriptors are thus necessary to unambiguously represent microstructural images. Our work provides crucial quantitative and qualitative in-formation that would aid in the selection of features for microstructural images. Featurized microstructures could then be utilized to build data-driven models for predictive control of microstructures and thereby properties of additively manufactured components.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Automatic Crack Segmentation and Feature Extraction in Electroluminescence Images of Solar Modules

The effect of cracks in solar cells on the long-term degradation of photovoltaic (PV) modules remains to be determined. To investigate this effect in future studies, it is necessary to quantitatively describe the crack features (e.g., length) and correlate them with module power loss. Electroluminescence (EL) imaging is a common technique for identifying cracks. However, it is currently challenging and time-consuming to identify cracks in a large number of EL images and quantify complex crack features by human inspection. This article introduces a fast semantic segmentation method (~0.18 s/cell) to automatically segment cracks from EL images and algorithms to extract crack features. Here we fine-tuned a UNet neural network model using pretrained VGG16 as the encoder and obtained an average F1 score of 0.875 and an intersection over union score of 0.782 on the testing set. With cracks and busbars segmented, we developed algorithms for extracting crack features, including the crack-isolated area, the brightness inside the isolated area, and the crack length. We also developed an automatic preprocessing tool for cropping individual cell images from EL images of PV modules (~0.72 s/module). Our codes are published as open-source an software, and our annotated dataset composed of various types of cells is published as a benchmark for crack segmentation in EL images.

14 SOLAR ENERGY↗
DOE OSTI2025

Basin-Scale Structural Features Database: Spatial Datasets to Support Carbon Storage Resource Assessments

Presentation slides on "Basin-Scale Structural Features Database: Spatial Datasets to Support Carbon Storage Resource Assessments" for CCUS 2025 Annual Meeting. The Basin-Scale Structural Features database contains a series of basin-scale spatial datasets representing structural features, including faults, fractures, folds, and earthquakes. Designed to support carbon storage feasibility and resources assessments for Carbon Capture and Storage (CCS) projects, the database leverages publicly available data resources from authoritative sources (e.g. US Geological Survey, State Geologic Surveys), and aims to help users better understand basin-scale structural features, as well as potential data gaps in areas with sparse information.

basin scale↗
DOE OSTI2021

Physics-Based Feature Extraction from Bulk Time-Series PMU Datasets for Event Detection

In this work, two physics-based feature extraction techniques are developed for bulk time-series phasor measurement unit (PMU) datasets collected from the field to train the machine learning model for anomaly detection. Two approaches have been developed to extract useful features for different types of events. An admittance-based feature extraction technique is developed to detect events that involve line outages and system topology variations. The developed algorithm extracts the system equivalent admittance variation. Additionally, Fielder’s Theory is utilized to further reduce the potential computation burden by sectionalizing large-scale grids and datasets into smaller areas. Second, an oscillation-based feature extraction technique is developed to detect low-frequency oscillations in power grids. The dominant oscillation modes in the grids are extracted using energy-sorted Prony analysis. The extracted dominant oscillation modes by the developed work exhibit a high fitting resolution. Finally, the developed techniques have been validated using large-scale and real-world datasets.

24 POWER TRANSMISSION AND DISTRIBUTION↗