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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Nonderivative modified gravity: a classification

We analyze the theories of gravity modified by a generic nonderivative potential built from the metric, under the minimal requirement of unbroken spatial rotations. Using the canonical analysis, we classify the potentials V according to the number of degrees of freedom (DoF) that propagate at the nonperturbative level. We then compare the nonperturbative results with the perturbative DoF propagating around Minkowski and FRW backgrounds. A generic V implies 6 propagating DoF at the non-perturbative level, with a ghost on Minkowski background. There exist potentials which propagate 5 DoF, as already studied in previous works. Here, no V with unbroken rotational invariance admitting 4 DoF is found. Theories with 3 DoF turn out to be strongly coupled on Minkowski background. Finally, potentials with only the 2 DoF of a massive graviton exist. Their effect on cosmology is simply equivalent to a cosmological constant. Potentials with 2 or 5 DoF and explicit time dependence appear to be a further viable possibility.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

ASTEROSEISMIC CLASSIFICATION OF STELLAR POPULATIONS AMONG 13,000 RED GIANTS OBSERVED BY KEPLER

Of the more than 150,000 targets followed by the Kepler Mission, about 10% were selected as red giants. Due to their high scientific value, in particular for Galaxy population studies and stellar structure and evolution, their Kepler light curves were made public in late 2011. More than 13,000 (over 85%) of these stars show intrinsic flux variability caused by solar-like oscillations making them ideal for large-scale asteroseismic investigations. We automatically extracted individual frequencies and measured the period spacings of the dipole modes in nearly every red giant. These measurements naturally classify the stars into various populations, such as the red giant branch, the low-mass (M/M{sub Sun} {approx}< 1.8) helium-core-burning red clump, and the higher-mass (M/M{sub Sun} {approx}> 1.8) secondary clump. The period spacings also reveal that a large fraction of the stars show rotationally induced frequency splittings. This sample of stars will undoubtedly provide an extremely valuable source for studying the stellar population in the direction of the Kepler field, in particular when combined with complementary spectroscopic surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Using ensembles and distillation to optimize the deployment of deep learning models for the classification of electronic cancer pathology reports

One of the goals of the Surveillance, Epidemiology, and End Results (SEER) program is to estimate incidence, prevalence, and mortality of all cancers. To that end, cancer registries across the country maintain a massive database of cancer pathology reports which contain rich information to understand cancer trends. However, these reports are stored in the form of unstructured text, and human annotators are required to read and extract relevant information. In this article, we show that existing deep learning models for automating information extraction from cancer pathology reports can be significantly improved by using ensemble model distillation. We found that by training multiple predictive models and transferring their knowledge to a single, low-resource model, we can reduce the number of highly confident wrong predictions. Our results show that our implemented methods could save 1000s of manual annotation hours.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2010

Classification of minimally doubled fermions

We propose a method to control the number of species of lattice fermions, which yields new classes of minimally doubled lattice fermions. We show it is possible to control the number of species by handling O(a) Wilson-term-like corrections in fermion actions, which we will term 'twisted-ordering method'. Using this method we obtain new minimally doubled actions with one exact chiral symmetry and exact locality. We classify the known minimally doubled fermions into two types based on the locations of the propagator poles in the Brillouin zone.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2014

Change detection and classification of land cover in multispectral satellite imagery using clustering of sparse approximations (CoSA) over learned feature dictionaries

Neuromimetic machine vision and pattern recognition algorithms are of great interest for landscape characterization and change detection in satellite imagery in support of global climate change science and modeling. We present results from an ongoing effort to extend machine vision methods to the environmental sciences, using adaptive sparse signal processing combined with machine learning. A Hebbian learning rule is used to build multispectral, multiresolution dictionaries from regional satellite normalized band difference index data. Land cover labels are automatically generated via our CoSA algorithm: Clustering of Sparse Approximations, using a clustering distance metric that combines spectral and spatial textural characteristics to help separate geologic, vegetative, and hydrologie features. We demonstrate our method on example Worldview-2 satellite images of an Arctic region, and use CoSA labels to detect seasonal surface changes. In conclusion, our results suggest that neuroscience-based models are a promising approach to practical pattern recognition and change detection problems in remote sensing.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Convolutional Neural Network-Based Protection-Zone Classification of Faults in Distribution Feeders with Photovoltaics.

Fault detection and isolation is critical for reliable operation of distribution systems. The ride-through requirements for the distributed energy resources (DER), mandated by the IEEE 1547-2018 standard, makes it challenging to use undervoltage (UV) conditions for fault detection. In addition, with low fault current contribution from these inverter-based DERs, the time-overcurrent relays are also less effective. Thus motivated, this paper presents a learning-based approach for fault detection and localization. A convolutional neural network (CNN)-based model is proposed which uses local voltage and current waveforms from DER locations and feeder substations, for training a zonal classifier. The classifier can be adopted into any relay-like device for discriminating between faults originating from different protection zones. The performance of the proposed approach was tested on publicly available test feeders with distributed photovoltaics (PVs).

Fault localization, convolutional neural networks,↗
DOE OSTI2020

Selective Sampling for Sensor Type Classification in Buildings

A key barrier to applying any smart technology to a building is the requirement of locating and connecting to the necessary resources among the thousands of sensing and control points, i.e., the metadata mapping problem. Existing solutions depend on exhaustive manual annotation of sensor metadata --- a laborious, costly, and hardly scalable process. To reduce the amount of manual effort required, this paper presents a multi-oracle selective sampling framework to leverage noisy labels from information sources with unknown reliability such as existing buildings, which we refer to as weak oracles, for metadata mapping. This framework involves an interactive process, where a small set of sensor instances are progressively selected and labeled for it to learn how to aggregate the noisy labels as well as to predict sensor types. Two key challenges arise in designing the framework, namely, weak oracle reliability estimation and instance selection for querying. To address the first challenge, we develop a clustering-based approach for weak oracle reliability estimation to capitalize on the observation that weak oracles perform differently in different groups of instances. For the second challenge, we propose a disagreement-based query selection strategy to combine the potential effect of a labeled instance on both reducing classifier uncertainty and improving the quality of label aggregation. We evaluate our solution on a large collection of real-world building sensor data from 5 buildings with more than 11,000 sensors of 18 different types. The experiment results validate the effectiveness of our solution, which outperforms a set of state-of-the-art baselines.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2018

Applying a hydrogeomorphic channel classification to understand spatial patterns in riparian vegetation

Here, we compared perennial riparian plant community composition among five a priori hydrogeomorphic channel types at 86 stream reaches in arid ephemeral watersheds. Floristic differences among channel types were assessed using PERMANOVA. GLM were used to identify species and functional groups whose abundance differed among channel types. Abiotic drivers of compositional differences were identified through correlations among biotic and abiotic matrices.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

SU-E-T-511: Inter-Rater Variability in Classification of Incidents in a New Incident Reporting System

Purpose To determine how consistent the results of different raters are when reviewing the same cases within the Radiation Oncology Incident Learning System (ROILS). Methods Three second-year medical physics graduate students filled out incident reports in spreadsheets set up to mimic ROILS. All students studied the same 33 cases and independently entered their assessments, for a total of 99 reviewed cases. The narratives for these cases were obtained from a published International Commission on Radiological Protection (ICRP) report which included shorter narratives selected from the Radiation Oncology Safety Information System (ROSIS) database. Each category of questions was reviewed to see how consistent the results were by utilizing free-marginal multirater kappa analysis. The percentage of cases where all raters shared full agreement or full disagreement was recorded to show which questions were answered consistently by multiple raters for a given case. The consistency among the raters was analyzed between ICRP and ROSIS cases to see if either group led to more reliable results. Results The categories where all raters agreed 100 percent in their choices were the event type (93.94 percent of cases 0.946 kappa) and the likelihood of the event being harmful to the patient (42.42 percent of cases 0.409 kappa). The categories where all raters disagreed 100 percent in their choices were the dosimetric severity scale (39.39 percent of cases 0.139 kappa) and the potential future toxicity (48.48 percent of cases 0.205 kappa). ROSIS had more cases where all raters disagreed than ICRP (23.06 percent of cases compared to 15.58 percent, respectively). Conclusion Despite reviewing the same cases, the results among the three raters was widespread. ROSIS narratives were shorter than ICRP, which suggests that longer narratives lead to more consistent results. This study shows that the incident reporting system can be optimized to yield more consistent results.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-J-224: Impact of 4D PET/CT On PERCIST Classification of Lung and Liver Metastases in NSLC and Colorectal Cancer

Purpose: To quantify the impact of 4D PET/CT on PERCIST metrics in lung and liver tumors in NSCLC and colorectal cancer patients. Methods: 32 patients presenting lung or liver tumors of 1–3 cm size affected by respiratory motion were scanned on a GE Discovery 690 PET/CT. The bed position with lesion(s) affected by motion was acquired in a 12 minute PET LIST mode and unlisted into 8 bins with respiratory gating. Three different CT maps were used for attenuation correction: a clinical helical CT (CT-clin), an average CT (CT-ave), and an 8-phase 4D CINE CT (CT-cine). All reconstructions were 3D OSEM, 2 iterations, 24 subsets, 6.4 Gaussian filtration, 192×192 matrix, non-TOF, and non-PSF. Reconstructions using CT-clin and CT-ave used only 3 out of the 12 minutes of the data (clinical protocol); all 12 minutes were used for the CT-cine reconstruction. The percent change of SUVbw-peak and SUVbw-max was calculated between PET-CTclin and PET-CTave. The same percent change was also calculated between PET-CTclin and PET-CTcine in each of the 8 bins and in the average of all bins. A 30% difference from PET-CTclin classified lesions as progressive metabolic disease (PMD) using maximum bin value and the average of eight bin values. Results: 30 lesions in 25 patients were evaluated. Using the bin with maximum SUVbw-peak and SUVbw-max difference, 4 and 13 lesions were classified as PMD, respectively. Using the average bin values for SUVbw-peak and SUVbw-max, 3 and 6 lesions were classified as PMD, respectively. Using PET-CTave values for SUVbw-peak and SUVbw-max, 4 and 3 lesions were classified as PMD, respectively. Conclusion: These results suggest that response evaluation in 4D PET/CT is dependent on SUV measurement (SUVpeak vs. SUVmax), number of bins (single or average), and the CT map used for attenuation correction.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2010

Classification of proton configurations of gas hydrate frameworks

All molecular configurations differing in the position of hydrogen atoms (protons) in hydrogen bonds and satisfying periodic boundary conditions have been calculated for the unit cells of CS-I, HS-III, and TS-IV gas hydrate frameworks. The configurations obtained are ranged according to the number of stronger trans-configurations of hydrogen-bound molecular pairs and according to the type of spatial symmetry. The configuration symmetry was analyzed taking into account the additional antisymmetry operation, which is related to the change in the direction of all hydrogen bonds. The strong dependence of the framework properties on the specific position of protons in H bonds is established.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2018

Classification of Energy States of the Exciton in Square Quantum Well

The energy states of the exciton in a single square GaAs-based quantum well are obtained from a numerical solution of the three-dimensional Schrödinger equation for the envelope of the exciton wave function. This equation is based on the exciton effective energy operator with a spherical approximation of the Luttinger Hamiltonian. The calculated states are classified based on the types of one-dimensional functions for the factorized form of the wave function. The upper limit for the energies of the exciton states in a quantum well is confirmed by the complex scaling method.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Facility Detection and Popularity Assessment from Text Classification of Social Media and Crowdsourced Data

Advances in technology have continually progressed our understanding of where people are, how they use the environment around them, and why they are at their current location. Having a better knowledge of when various locations become popular through space and time could have large impacts on research fields like urban dynamics and energy consumption. In this paper, we discuss the ability to identify and locate various facility types (e.g. restaurant, airport, stadiums) using social media, and assess methods in determining when these facilities become popular over time. We use natural language processing tools and machine learning classifiers to interpret geotagged Twitter text and determine if a user is seemingly at a location of interest when the tweet was sent. On average our classifiers are approximately 85% accurate varying across multiple facility types, with a peak precision of 98%. By using these methods to classify unstructured text, geotagged social media data can be an extremely useful tool to better understanding the composition of places and how and when people use them.

crowd-sourced data↗
DOE OSTI2025

Unlocking Scholarly Insights: Leveraging Machine Learning Approaches for Citation Analysis and Intent Classification

Publicly funded organizations, notably institutions like the Los Alamos National Laboratory (LANL), are deeply vested in acquiring robust productivity metrics to gauge the entirety of their research output. Motivated by the imperative to enhance institutional productivity assessment, this study investigates the utilization of Large Language Models (LLM) such as BERT-based models, as well as local LLaMa-30b-instruct and Mixtral-8x7b-instruct architectures for classifying type of URL referenced resources in academic papers such as software, dataset, as well as authorship intent. Challenges in discerning resource types from context are highlighted, along with the potential of BERT and LLMs to address these challenges. Through comprehensive analysis, this research unveils a notable surge in documents featuring URL citations, indicative of the escalating importance of digital resources in scholarly publications. Moreover, citations to datasets and software demonstrate consistent growth over time, underscoring their increasing significance. Our findings also reveal that LANL authors contribute substantially to accessible science, comprising about 10% of dataset and software mentions in LANL

Large Language Models, BERT, citation classificati↗
DOE OSTI2023

Machine Learning Benchmarks for the Classification of Equivalent Circuit Models from Electrochemical Impedance Spectra

Analysis of Electrochemical Impedance Spectroscopy (EIS) data for electrochemical systems often consists of defining an Equivalent Circuit Model (ECM) using expert knowledge and then optimizing the model parameters to deconvolute various resistance, capacitive, inductive, or diffusion responses. For small data sets, this procedure can be conducted manually; however, it is not feasible to manually define a proper ECM for extensive data sets with a wide range of EIS responses. Automatic identification of an ECM would substantially accelerate the analysis of large sets of EIS data. We showcase machine learning methods to classify the ECMs of 9,300 impedance spectra provided by QuantumScape for the BatteryDEV hackathon. The best-performing approach is a gradient-boosted tree model utilizing a library to automatically generate features, followed by a random forest model using the raw spectral data. A convolutional neural network using boolean images of Nyquist representations is presented as an alternative, although it achieves a lower accuracy. We publish the data and open source the associated code. The approaches described in this article can serve as benchmarks for further studies. A key remaining challenge is the identifiability of the labels, underlined by the model performances and the comparison of misclassified spectra.

25 ENERGY STORAGE↗