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DOE OSTI2022

Identifications of RR Lyrae Stars and Quasars from the Simulated Data of Mephisto-W Survey

We have investigated the feasibilities and accuracies of the identifications of RR Lyrae stars and quasars from the simulated data of the Multi-channel Photometric Survey Telescope (Mephisto) W Survey. Based on the variable sources light curve libraries from the Sloan Digital Sky Survey (SDSS) Stripe 82 data and the observation history simulation from the Mephisto-W Survey Scheduler, we have simulated the uvgriz multi-band light curves of RR Lyrae stars, quasars and other variable sources for the first-year observation of Mephisto W Survey. We have applied the ensemble machine learning algorithm Random Forest Classifier (RFC) to identify RR Lyrae stars and quasars, respectively. We build training and test samples and extract ~150 features from the simulated light curves and train two RFCs respectively for the RR Lyrae star and quasar classification. We find that, our RFCs are able to select the RR Lyrae stars and quasars with remarkably high precision and completeness, with purity = 95.4% and completeness = 96.9% for the RR Lyrae RFC and purity = 91.4% and completeness = 90.2% for the quasar RFC. In conclusion, we have also derived relative importances of the extracted features utilized to classify RR Lyrae stars and quasars.

(galaxies:) quasars: general↗

DOE OSTI2024

Deep Learning for Fish Identification from Sonar Data (CRADA 481 Final Report)

In eastern regions of the United States, the American eel is a species of management and regulatory concern because of significant population declines, despite the species’ previous abundance in all tributaries of rivers flowing into the Atlantic Ocean. The American eel is also a candidate for listing under the U.S. Endangered Species Act. While hydropower construction and operation are only one of several factors contributing to this population decline, such a listing could impose additional regulatory challenges for a large number of hydropower projects. In this CRADA project, we improved technologies for identifying migrating eels with the goal of reducing the cost and time required for future American eel hydropower impact assessment and mitigation studies, while maintaining accuracy. We built on results from a previous FOA project (FOA# DE-FOA-0001662), led by the Electric Power Research Institute (EPRI), which developed a highly accurate, deep-learning method for identifying migrating eels from imaging sonar data. The current study aimed to further optimize this deep-learning model, originally designed for image classification, and to develop an object detection software capable of identifying fish from sonar videos in real time, enabling the detection of events like fish migrations and specific species, such as the American eel, at hydropower dams. The data conversion algorithms were packaged as software with a graphical user interface, and the software is evaluated by external collaborators. We focused on the American eel in this project and explored the transferability of the developed deep learning models to the sea lamprey, given the similar body shape and swimming behavior between the two species.

13 HYDRO ENERGY↗

DOE OSTI2023

The miniJPAS survey quasar selection – II. Machine learning classification with photometric measurements and uncertainties

Astrophysical surveys rely heavily on the classification of sources as stars, galaxies, or quasars from multiband photometry. Surveys in narrow-band filters allow for greater discriminatory power, but the variety of different types and redshifts of the objects present a challenge to standard template-based methods. In this work, which is part of a larger effort that aims at building a catalogue of quasars from the miniJPAS survey, we present a machine learning-based method that employs convolutional neural networks (CNNs) to classify point-like sources including the information in the measurement errors. We validate our methods using data from the miniJPAS survey, a proof-of-concept project of the Javalambre Physics of the Accelerating Universe Astrophysical Survey (J-PAS) collaboration covering ∼1 deg 2 of the northern sky using the 56 narrow-band filters of the J-PAS survey. Due to the scarcity of real data, we trained our algorithms using mocks that were purpose-built to reproduce the distributions of different types of objects that we expect to find in the miniJPAS survey, as well as the properties of the real observations in terms of signal and noise. We compare the performance of the CNNs with other well-established machine learning classification methods based on decision trees, finding that the CNNs improve the classification when the measurement errors are provided as inputs. The predicted distribution of objects in miniJPAS is consistent with the putative luminosity functions of stars, quasars, and unresolved galaxies. Our results are a proof of concept for the idea that the J-PAS survey will be able to detect unprecedented numbers of quasars with high confidence.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS2011

Numerical Treatment of Degenerate Diffusion Equations via Feller's Boundary Classification, and Applications

A numerical method is devised to solve a class of linear boundary-value problems for one-dimensional parabolic equations degenerate at the boundaries. Feller theory, which classifies the nature of the boundary points, is used to decide whether boundary conditions are needed to ensure uniqueness, and, if so, which ones they are. The algorithm is based on a suitable preconditioned implicit finite-difference scheme, grid, and treatment of the boundary data. Second-order accuracy, unconditional stability, and unconditional convergence of solutions of the finite-difference scheme to a constant as the time-step index tends to infinity are further properties of the method. Several examples, pertaining to financial mathematics, physics, and genetics, are presented for the purpose of illustration.

Cacio, Emanuela↗

NASA NTRS2020

Robust Algorithm for Estimating Total Suspended Solids (TSS) in Inland and Nearshore Coastal Waters

One of the challenging tasks in modern aquatic remote sensing is the retrieval of near-surface concentrations of Total Suspended Solids (TSS). This study aims to present a Statistical, inherent Optical property (IOP) -based, and muLti-conditional Inversion proceDure (SOLID) for enhanced retrievals of satellite-derived TSS under a wide range of in-water bio-optical conditions in rivers, lakes, estuaries, and coastal waters. In this study, using a large in situ database (N > 3500), the SOLID model is devised using a three-step procedure: (a) water-type classification of the input remote sensing reflectance (R(sub rs)), (b) retrieval of particulate backscattering (b(sub bp)) in the red or near-infrared (NIR) regions using semi-analytical, machine-learning, and empirical models, and (c) estimation of TSS from b(sub bp) via water-type-specific empirical models. Using an independent subset of our in situ data (N = 2729) with TSS ranging from 0.1 to 2626.8 [g/m (exp 3)], the SOLID model is thoroughly examined and compared against several state-of-the-art algorithms (Miller and McKee, 2004; Nechad et al., 2010; Novoa et al., 2017; Ondrusek et al., 2012; Petus et al., 2010). We show that SOLID outperforms all the other models to varying degrees, i.e., from 10 to > 100%, depending on the statistical attributes (e.g., global versus water-type-specific metrics). For demonstration purposes, the model is implemented for images acquired by the MultiSpectral Imager aboard Sentinel-2A/B over the Chesapeake Bay, San-Francisco-Bay-Delta Estuary, Lake Okeechobee, and Lake Taihu. To enable generating consistent, multimission TSS products, its performance is further extended to, and evaluated for, other missions, such as the Ocean and Land Color Instrument (OLCI), Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), and Operational Land Imager (OLI). Sensitivity analyses on uncertainties induced by the atmospheric correction indicate that 10% uncertainty in Rrs leads to < 20% uncertainty in TSS retrievals from SOLID. While this study suggests that SOLID has a potential for producing TSS products in global coastal and inland waters, our statistical analysis certainly verifies that there is still a need for improving retrievals across a wide spectrum of particle loads.

Total suspended solids↗

NASA NTRS2004

Toward a Global Map of Raindrop Size Distributions: Rain-Type Classification and Its Implications for Validating Global Rainfall Products - Part 1

Variability in the global distribution of precipitation is recognized as a key element in assessing the impact of climate change for life on earth. The response of precipitation to climate forcings is, however, poorly understood because of discrepancies in the magnitude and sign of climatic trends in satellite-based rainfall estimates. Quantifying and ultimately removing these biases is critical for studying the response of the hydrologic cycle to climate change. In addition, estimates of random errors owing to variability in algorithm assumptions on local spatial and temporal scales are critical for establishing how strongly their products should be weighted in data assimilation or model validation applications and for assigning a level of confidence to climate trends diagnosed from the data. This paper explores the potential for refining assumed drop size distributions (DSDs) in global radar rainfall algorithms by establishing a link between satellite observables and information gleaned from regional validation experiments where polarimetric radar, Doppler radar, and disdrometer measurements can be used to infer raindrop size distributions. By virtue of the limited information available in the satellite retrieval framework, the current method deviates from approaches adopted in the ground-based radar community that attempt to relate microphysical processes and resultant DSDs to local meteorological conditions. Instead, the technique exploits the fact that different microphysical pathways for rainfall production are likely to lead to differences in both the DSD of the resulting raindrops and the three-dimensional structure of associated radar reflectivity profiles. Objective rain-type classification based on the complete three-dimensional structure of observed reflectivity profiles is found to partially mitigate random and systematic errors in DSDs implied by differential reflectivity measurements. In particular, it is shown that vertical and horizontal reflectivity structure obtained from spaceborne radar can be used to reproduce significant differences in Z(sub dr) between the easterly and westerly climate regimes observed in the Tropical Rainfall Measuring Mission Large-scale Biosphere-Atmosphere (TRMM-LBA) field experiment as well as the even larger differences between Amazonian rainfall and that observed in eastern Colorado. As such, the technique offers a potential methodology for placing locally observed DSD information into a global framework.

L'Ecuyer, Tristan S.↗

NASA NTRS1989

Unsupervised classification of scattering behavior using radar polarimetry data

The use of an imaging radar polarimeter data for unsupervised classification of scattering behavior is described by comparing the polarization properties of each pixel in a image to that of simple classes of scattering such as even number of reflections, odd number of reflections, and diffuse scattering. For example, when this algorithm is applied to data acquired over the San Francisco Bay area in California, it classifies scattering by the ocean as being similar to that predicted by the class of odd number of reflections, scattering by the urban area as being similar to that predicted by the class of even number of reflections, and scattering by the Golden Gate Park as being similar to that predicted by the diffuse scattering class. It also classifies the scattering by a lighthouse in the ocean and boats on the ocean surface as being similar to that predicted by the even number of reflections class, making it easy to identify these objects against the background of the surrounding ocean. The algorithm is also applied to forested areas and shows that scattering from clear-cut areas and agricultural fields is mostly similar to that predicted by the odd number of reflections class, while the scattering from tree-covered areas generally is classified as being a mixture of pixels exhibiting the characteristics of all three classes, although each pixel is identified with only a single class.

Van Zyl, Jakob J.↗

NASA NTRS1992

Feature selection for neural networks using Parzen density estimator

A feature selection method for neural networks is proposed using the Parzen density estimator. A new feature set is selected using the decision boundary feature selection algorithm. The selected feature set is then used to train a neural network. Using a reduced feature set, an attempt is made to reduce the training time of the neural network and obtain a simpler neural network, which further reduces the classification time for test data.

Lee, Chulhee↗

DOE OSTI2024

Dual X-ray computed tomography-aided classification of melt pool boundaries and flaws in crept additively manufactured parts

In metal additive manufacturing (AM), understanding the process-structure-performance relationships requires a combination of multi-scale characterization techniques that allows for the measurement of the melt pool shape and boundary and classifying various defects and flaws in the AM parts. Such approaches can be destructive, only 2D in nature, or have a small field of view and can be complex to co-register and analyze. Here, in this work, we present a non-destructive 3D inspection technique that employs dual-energy X-ray computed tomography (XCT) along with a model-based iterative reconstruction (MBIR) and a new segmentation algorithm. The proposed approach and algorithm are not only capable of classifying and quantifying flaws such as pores, cracks, and inclusions, but they also allow for the extraction of microstructural features such as melt pool boundaries (MPB) and melt pool regions (MPR), that can help understand process-structure-performance relationships for alloys under study. As an exemplar application, we employed the method for characterization of an additively manufactured aluminum alloy crept under tensile stress at 300 °C for 1064 h. Our results demonstrate high quality segmentation and classification of various flaws and MPB and MPR, for the first time, using 3D X-ray CT inspection. The delineated MPB and MPR in the crept samples reveal the preferential growth paths of cracks that formed during creep deformation. The technique was used for successfully quantifying the characteristics (number of defects, their density, volume fraction, etc.) of the manufacturing-induced pores and creep-induced cracks, which is necessary to better understand the creep failure mechanisms of the material.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Novel Application of Machine Learning Techniques for Rapid Source Apportionment of Aerosol Mass Spectrometer Datasets

In this work, we apply machine learning approaches sparse multinomial logistic regression to classify aerosol mass spectrometer (AMS) unit mass resolution (UMR) data followed by an ensemble regression technique for source apportionment of organic aerosols (OA). The classifier was trained on 60 well characterized laboratory and positive matrix factorization (PMF) deconvolved reference spectra to identify eight OA types. These include four laboratory-derived secondary organic aerosol (SOA) spectra, which include isoprene photooxidation SOA, isoprene epoxydiols (IEPOX) SOA, a monoterpene SOA type that includes a-pinene and ß-pinene SOA, and aromatic SOA from oxidation of naphthalene and m-xylene precursors, as well as PMF deconvolved spectra for three primary organic aerosol (POA) types, namely, hydrocarbon-like organic aerosol (HOA), biomass burning organic aerosol (BBOA), and cooking OA (COA), and a more oxidized oxygenated OA type (MO-OOA). A 5-fold cross-validation strategy, repeated 10 times, was used to assess the classifier’s performance. The classifier had high classification accuracy for COA, aromatic SOA, and isoprene SOA spectra but incorrectly classified ~9% by number of MO-OOA spectra as BBOA, 12% of BBOA spectra as HOA (and vice versa), and 18% of IEPOX-SOA spectra as aromatic SOA. Next, an ensemble regression model was trained on an artificially generated dataset consisting of mixtures of different OA types to assess its ability to predict fractional mass abundances from classification probabilities of various OA species obtained from the multinomial logistic regression classifier trained on the reference spectra. Ultimately, the proposed approach was applied for source apportionment of aircraft-based AMS measurements of OA UMR spectra during the HI-SCALE field campaign. On two representative days (May 6th and 18th, 2016), the algorithm determined that ~50-60% of OA by mass was MO-OOA, which represented a highly aged organic aerosol mixture from different sources. On both days, BBOA was determined to contribute less than 10% to OA by mass. However, on May 18th, the aromatic SOA fraction was higher compared to that on May 6th. The proposed approach is capable of rapidly analyzing AMS data in real time, making it suitable for applications where rapid source apportionment of AMS OA spectra is desirable.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1986

Optical processing and Space Station automation

Optical information processing research aimed at Space Station automation applications is reviewed. The emphasis of the NASA Ames Research Center program is on intelligent optical pattern recognition and optical control processing. Attention is given to the primary functions of an overall scene understanding system: distortion-invariant optical feature generation, dimensionality reduction, object classification, and contextual information processing. A method of using synthetic discriminant functions to facilitate learning in a high-speed optical correlator is described. A discussion is presented of candidate analog and digital architectures for the optical implementation of state-estimation algorithms needed for the control of high-dimension dynamic systems. The multivariate system chosen for the optical control technology demonstration - a segmented, adaptive mirror and interferometrically based wavefront sensor - is also described.

Ennis, D. J.↗

NASA NTRS2013

Texturecam: A Smart Camera for Microscale, Mesoscale, and Deep Space Applications

The TextureCam project is developing a 'smart camera' that can classify geologic surfaces in planetary images. This would allow autonomous spacecraft to collect data opportunisitcally during intervals between communications with Earth, such as during long traverses. Its surface classifications can identify new targets that were not anticipated in advance. The spacecraft might use this information to target these features with high-resolution instruments such as spectrometers nd narrow-field cameras. Classifications could also inform data 'triage' decisions, identifying high value images for prioritized downlink. Finally, the surface classification can serve as compressed maps of image content. Each of these strategies can improve the science data returned at each command cycle and speed reconnaissance during site survey and astrobiology investigation. Our first year of development has completed the image analysis algorithms and validated them in software tests. Here we survey these initial results and explore several application areas relevant to Mars and beyond.

Hyperspectral imagery↗

DOE OSTI2023

Classification based anomaly detection

An example device includes processing circuitry and a memory. The memory includes instructions that cause the device to perform various functions. The functions include receiving datastreams from a plurality of sensors of a high performance computing system, classifying each datastream of the each sensor to one of a plurality of datastream models, selecting an anomaly detection algorithm from a plurality of anomaly detection algorithms for each datastream, determining parameters of the each anomaly detection algorithm, determining an anomaly threshold for each datastream, and generating an indication that the sensor associated with the datastream is acting anomalously.

Serebryakov, Sergey↗

DOE OSTI2024

MedNAS: Multiscale Training-Free Neural Architecture Search for Medical Image Analysis

Deep neural networks have demonstrated impressive results in medical image analysis, but designing suitable architectures for each specific task is expertise dependent and time consuming. Neural architecture search (NAS) offers an effective means of discovering architectures. It has been highly successful in numerous applications, particularly in natural image classification. Yet, medical images possess unique characteristics, such as small regions and a wide variety of lesion sizes, that differentiate them from natural images. Furthermore, most current NAS methods struggle with high computational costs, especially when dealing with high-resolution image datasets. In this article, we present a novel evolutionary NAS method called multiscale training-free neural architecture search (MSTF-NAS) to address these challenges. Specifically, to accommodate the broad range of lesion region sizes in disease diagnosis, we develop a new reduction cell search space that enables the search algorithm to explicitly identify the optimal scale combination for multiscale feature extraction. Further, to overcome the issue of high computational costs, we utilize training-free indicators as performance measures for candidate architectures, which allows us to search for the optimal architecture more efficiently. More specifically, by considering the capability and simplicity of various networks, we formulate a multiobjective optimization problem that involves two training-free indicators and model complexity for candidate architectures. Extensive experiments on a large medical image benchmark and a publicly available breast cancer detection dataset are conducted. The empirical results demonstrate that our MSTF-NAS outperforms both human-designed architectures and current state-of-the-art NAS algorithms on both datasets, indicating the effectiveness of our proposed method.

60 APPLIED LIFE SCIENCES↗

NASA NTRS2003

The Land Use and Land Cover Dichotomy: A Comparison of Two Land Classification Systems in Support of Urban Earth Science Applications

One is likely to read the terms 'land use' and 'land cover' in the same sentence, yet these concepts have different origins and different applications. Land cover is typically analyzed by earth scientists working with remotely sensed images. Land use is typically studied by urban planners who must prescribe solutions that could prevent future problems. This apparent dichotomy has led to different classification systems for land-based data. The works of earth scientists and urban planning practitioners are beginning to come together in the field of spatial analysis and in their common use of new spatial analysis technology. In this context, the technology can stimulate a common 'language' that allows a broader sharing of ideas. The increasing amount of land use and land cover change challenges the various efforts to classify in ways that are efficient, effective, and agreeable to all groups of users. If land cover and land uses can be identified by remote methods using aerial photography and satellites, then these ways are more efficient than field surveys of the same area. New technology, such as high-resolution satellite sensors, and new methods, such as more refined algorithms for image interpretation, are providing refined data to better identify the actual cover and apparent use of land, thus effectiveness is improved. However, the closer together and the more vertical the land uses are, the more difficult the task of identification is, and the greater is the need to supplement remotely sensed data with field study (in situ). Thus, a number of land classification methods were developed in order to organize the greatly expanding volume of data on land characteristics in ways useful to different groups. This paper distinguishes two land based classification systems, one developed primarily for remotely sensed data, and the other, a more comprehensive system requiring in situ collection methods. The intent is to look at how the two systems developed and how they can work together so that land based information can be shared among different users and compared over time.

McAllister, William K.↗

DOE OSTI2020

First Cosmology Results using Supernovae Ia from the Dark Energy Survey: Survey Overview, Performance, and Supernova Spectroscopy

We present details on the observing strategy, data-processing techniques, and spectroscopic targeting algorithms for the first three years of operation for the Dark Energy Survey Supernova Program (DES-SN). This five-year program using the Dark Energy Camera mounted on the 4 m Blanco telescope in Chile was designed to discover and follow supernovae (SNe) Ia over a wide redshift range (0.05 < z < 1.2) to measure the equation-of-state parameter of dark energy. We describe the SN program in full: strategy, observations, data reduction, spectroscopic follow-up observations, and classification. From three seasons of data, we have discovered 12,015 likely SNe, 308 of which have been spectroscopically confirmed, including 251 SNe Ia over a redshift range of 0.017 < z < 0.85. We determine the effective spectroscopic selection function for our sample and use it to investigate the redshift-dependent bias on the distance moduli of SNe Ia we have classified. The data presented here are used for the first cosmology analysis by DES-SN (“DES-SN3YR”), the results of which are given in Dark Energy Survey Collaboration et al. The 489 spectra that are used to define the DES-SN3YR sample are publicly available at https://des.ncsa.illinois.edu/releases/sn.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2019

Machine Learning for Solar Technology Portfolio Management

Technology readiness level (TRL) labeling and forecasting today is done entirely manually. A computationally-assisted model of a TRL-like classification for estimation and tracking could enable a research and development (R&D) portfolio manager to more accurately estimate the relative level of development for technologies in that portfolio. This report describes the work to understand readiness level systems, train a machine learning algorithm to recognize textual indicators of particular readiness levels within written material from carefully chosen case studies in renewable technology development, and apply that algorithm to predict the readiness levels of other technology development cases. We account for the results of each step of the work conducted, including the predictive accuracy of the trained model: 54% correct, within a +/-1 readiness level error threshold and a root mean squared error of 1.88. Great potential remains to improve and apply this model for management of individual projects and across portfolios.

14 SOLAR ENERGY↗

DOE OSTI2019

Machine Learning for Solar Technology Portfolio Management

14 SOLAR ENERGY↗