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Results for “affinity propagation”

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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14 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Machine learning-powered data cleaning for LEGEND: a semi-supervised approach using affinity propagation and support vector machines

Neutrinoless double-beta decay ($0\nu\beta\beta$) is a rare nuclear process that, if observed, will provide insight into the nature of neutrinos and help explain the matter-antimatter asymmetry in the Universe. The large enriched germanium experiment for neutrinoless double-beta decay (LEGEND) will operate in two phases to search for $0\nu\beta\beta$. The first (second) stage will employ 200 (1000) kg of High-Purity Germanium (HPGe) enriched in 76 Ge to achieve a half-life sensitivity of 10 27 (10 28 ) years. In this study, we present a semi-supervised data-driven approach to remove non-physical events captured by HPGe detectors powered by a novel artificial intelligence model. We utilize affinity propagation to cluster waveform signals based on their shape and a support vector machine to classify them into different categories. We train, optimize, and test our model on data taken from a natural abundance HPGe detector installed in the Full Chain Test experimental stand at the University of North Carolina at Chapel Hill. We demonstrate that our model yields a maximum sacrifice of physics events of $0.024 ^{+0.004}_{-0.003} \%$ after data cleaning. Our model is being used to accelerate data cleaning development for LEGEND-200 and will serve to improve data cleaning procedures for LEGEND-1000.

artificial intelligence↗
DOE OSTI2022

Deep Learning Based Frequency Stability Assessment in Power Grid with High Renewables

Frequency stability assessment is one critical aspect of power system security assessment. Traditional N-1 screening method is based on the simulations of a few typical daily and seasonal operation scenarios. However, the increasing integration of inverter-based renewables and the retirement of conventional synchronous generators result in decreasing system inertia and growing complexity of system operating conditions. Selecting a few typical operation scenarios cannot cover all operating conditions, and the time-domain simulation of all operation conditions requires tremendous time. This paper proposes a more efficient frequency stability assessment method based on deep learning. The affinity propagation clustering algorithm is used to divide the dataset into different clusters, so the selected dataset for training can cover the diversified operating conditions as much as possible. Also, feature normalization is applied to both the training dataset and testing dataset in order to remove any unnecessary bias. Especially, trained model based on full dataset normalization has bounded error in the prediction. The case study on the reduced 240-bus WECC system demonstrates that the proposed method can predict accurate frequency nadir with limited training dataset. The deep learning model using the revised feature normalization can predict more accurate frequency nadir than that using the traditional feature normalization and has very small maximum prediction error.

affinity propagation↗
DOE OSTI2025

Web-based wide-area monitoring platform for ringdown and clustering analytics in power systems

This paper introduces an open-source research platform for monitoring the Mexican interconnected power grid, allowing real-time processing and information extraction of the grid’s dynamic condition. Moreover, the platform is a Python-based development that embeds different ringdown and clustering analytics tools. In the case of ringdown analysis, the modal information can be extracted using some of the most known algorithms, i.e., Prony analysis, eigensystem realization algorithm (ERA), and matrix pencil (MP). For clustering analysis, the coherent behaviour of generator and non-generator buses is provided by applying recent state-of-the-art techniques such as affinity propagation, K-means, hierarchical agglomerative clustering, and typicality data analysis. The results of up to 93 PMUs show that this open-source platform suits researchers’ and engineers’ power system dynamic analysis requirements.

Clustering↗
DOE OSTI2021

Iterative self-organizing SCEne-LEvel sampling (ISOSCELES) for large-scale building extraction

Convolutional neural networks (CNN) provide state-of-the-art performance in many computer vision tasks, including those related to remote-sensing image analysis. Successfully training a CNN to generalize well to unseen data, however, requires training on samples that represent the full distribution of variation of both the target classes and their surrounding contexts. With remote sensing data, acquiring a sufficiently representative training set is a challenge due to both the inherent multi-modal variability of satellite or aerial imagery and the general high cost of labeling data. To address this challenge, we have developed ISOSCELES, an Iterative Self-Organizing SCEne LEvel Sampling method for hierarchical sampling of large image sets. Using affinity propagation, ISOSCELES automates the selection of highly representative training images. Compared to random sampling or using available reference data, the distribution of the training is principally data driven, reducing the chance of oversampling uninformative areas or undersampling informative ones. In comparison to manual sample selection by an analyst, ISOSCELES exploits descriptive features, spectral and/or textural, and eliminates human bias in sample selection. Using a hierarchical sampling approach, ISOSCELES can obtain a training set that reflects both between-scene variability, such as in viewing angle and time of day, and within-scene variability at the level of individual training samples. We verify the method by demonstrating its superiority to stratified random sampling in the challenging task of adapting a pre-trained model to a new image and spatial domain for country-scale building extraction. Using a pair of hand-labeled training sets comprising 1,987 sample image chips, a total of 496,000,000 individually labeled pixels, we show, across three distinct model architectures, an increase in accuracy, as measured by F1-score, of 2.2–4.2%.

42 ENGINEERING↗
DOE OSTI2023

A multiscale phase field fracture approach based on the non-affine microsphere model for rubber-like materials

Rubber-like materials have a broad scope of applications due to their unique properties like high stretchability and increased toughness. Hence, computational models for simulating their fracture behavior are paramount for designing them against failures. In this study, the phase field fracture approach is integrated with a multiscale polymer model for predicting the fracture behavior in elastomers. At the microscale, damaged polymer chains are modeled to be made up of a number of elastic chain segments pinned together. Using the phase field approach, the damage in the chains is represented using a continuous variable. Both the bond stretch internal energy and the entropic free energy of the chain are assumed to drive the damage, and the advantages of this assumption are expounded. A framework for utilizing the non-affine microsphere model for damaged systems is proposed here by considering the minimization of a hypothetical undamaged free energy, ultimately connecting the chain stretch to the macroscale deformation gradient. At the macroscale, a thermodynamically consistent formulation is derived in which the total dissipation is assumed to be mainly due to the rupture of molecular bonds. Using a monolithic scheme, the proposed model is numerically implemented and the resulting three-dimensional simulation predictions are compared with existing experimental data. The capability of the model to qualitatively predict the propagation of complex crack paths and quantitatively estimate the overall fracture behavior is verified. Additionally, the effect of the length scale parameter on the predicted fracture behavior is studied for an inhomogeneous system.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1987

On self-similarity of crack layer

The crack layer (CL) theory of Chudnovsky (1986), based on principles of thermodynamics of irreversible processes, employs a crucial hypothesis of self-similarity. The self-similarity hypothesis states that the value of the damage density at a point x of the active zone at a time t coincides with that at the corresponding point in the initial (t = 0) configuration of the active zone, the correspondence being given by a time-dependent affine transformation of the space variables. In this paper, the implications of the self-similarity hypothesis for qusi-static CL propagation is investigated using polystyrene as a model material and examining the evolution of damage distribution along the trailing edge which is approximated by a straight segment perpendicular to the crack path. The results support the self-similarity hypothesis adopted by the CL theory.

Botsis, J.↗
DOE OSTI2023

Mechanism of Stoichiometrically Governed Titanium Oxide Brownian Tree Formation on Stepped Au(111)

Previously observed formation of substoichiometric titanium oxide dendritic structures across terraces of Au(111) is computationally studied and shown to follow the classical fractal formation mechanism of diffusion-limited aggregation (DLA). Globally optimized gas-phase oxide cluster structures are sampled in a variety of landing formations on gold surfaces and shown to favor isomers driving polymerization to Brownian tree fractal structures. Mobility of Ti 3 O 5 monomers is shown to be extremely high, with diffusion barriers of 0.21 eV or less. Through bonding stabilization, polymerization of these monomers is energetically favorable and irreversible on the 111 terrace but geometrically impossible to propagate along the step edge. Simulated scanning tunneling microscopy (STM) images show strong similarity to experiment. By contrast, observation of Ti 3 O 6 aggregating as wires along step edges is explained by the affinity of oxygen to step edges and statistical arguments for aggregation entropy at the step, in addition to low barriers for monomer diffusion and polymerization.

36 MATERIALS SCIENCE↗
NASA NTRS1994

Calcium influences sensitivity to growth inhibition induced by a cell surface sialoglycopeptide

While studies concerning mitogenic factors have been an important area of research for many years, much less is understood about the mechanisms of action of cell surface growth inhibitors. We have purified an 18 kDa cell surface sialoglycopeptide growth inhibitor (CeReS-18) which can reversibly inhibit the proliferation of diverse cell types. The studies discussed in this article show that three mouse keratinocyte cell lines exhibit sixty-fold greater sensitivity than other fibroblasts and epithelial-like cells to CeReS-18-induced growth inhibition. Growth inhibition induced by CeReS-18 treatment is a reversible process, and the three mouse keratinocyte cell lines exhibited either single or multiple cell cycle arrest points, although a predominantly G0/G1 cell cycle arrest point was exhibited in Swiss 3T3 fibroblasts. The sensitivity of the mouse keratinocyte cell lines to CeReS-18-induced growth inhibition was not affected by the degree of tumorigenic progression in the cell lines and was not due to differences in CeReS-18 binding affinity or number of cell surface receptors per cell. However, the sensitivity of both murine fibroblasts and keratinocytes could be altered by changing the extracellular calcium concentration, such that increased extracellular calcium concentrations resulted in decreased sensitivity to CeReS-18-induced proliferation inhibition. Thus the increased sensitivity of the murine keratinocyte cell lines to CeReS-18 could be ascribed to the low calcium concentration used in their propagation. Studies are currently under way investigating the role of calcium in CeReS-18-induced growth arrest. The CeReS-18 may serve as a very useful tool to study negative growth control and the signal transduction events associated with cell cycling.

NASA Discipline Cell Biology↗
DOE OSTI2023

Exact relationships between the GW approximation and equation-of-motion coupled-cluster theories through the quasi-boson formalism

We describe the relationship between the GW approximation and various equation-of-motion (EOM) coupled-cluster (CC) theories. We demonstrate the exact equivalence of the G0W0 approximation and the propagator theory for an electron–boson problem in a particular excitation basis. From there, we establish equivalence within the quasi-boson picture to the IP+EA-EOM unitary CC propagator. We analyze the incomplete description of screening provided by the standard similarity-transformed IP+EA-EOM-CC and the recently introduced G0W0 Tamm–Dancoff approximation. We further consider the approximate decoupling of IP and EA sectors in EOM-CC treatments and devise the analogous particle–hole decoupling approach for the G0W0 approximation. Finally, we numerically demonstrate the exact relationships and magnitude of the approximations in the calculations of a set of molecular ionization potentials and electron affinities.

Chemistry↗
DOE OSTI2024

Effects of SARS-CoV-2 Main Protease Mutations at Positions L50, E166, and L167 Rendering Resistance to Covalent and Noncovalent Inhibitors

SARS-CoV-2 propagation under nirmatrelvir and ensitrelvir pressure selects for main protease (MPro) drug-resistant mutations E166V (DRM2), L50F/E166V (DRM3), E166A/L167F (DRM4), and L50F/E166A/L167F (DRM5). DRM2-DRM5 undergoes N-terminal autoprocessing to produce mature MPro with dimer dissociation constants (K dimer ) 2–3 times larger than that of the wildtype. Co-selection of L50F restores catalytic activity of DRM2 and DRM4 from ~10 to 30%, relative to that of the wild-type enzyme, without altering K dimer . Binding affinities and thermodynamic profiles that parallel the drug selection pressure, exhibiting significant decreases in affinity through entropy/enthalpy compensation, were compared with GC373. Reorganization of the active sites due to mutations observed in the inhibitor-free DRM3 and DRM4 structures as compared to MPro WT may account for the reduced binding affinities, although DRM2 and DRM3 complexes with ensitrelvir are almost identical to MPro WT -ensitrelvir. In conclusion, chemical reactivity changes of the mutant active sites due to differences in electrostatic and protein dynamics effects likely contribute to losses in binding affinities.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2007

Accuracy Analysis on Large Blocks of High Resolution Images

Although high altitude frequencies effects are removed at the time of basic image generation, low altitude (Yaw) effects are still present in form of affinity/angular affinity. They are effectively removed by additional parameters. Bundle block adjustment based on properly weighted ephemeris/altitude quaternions (BBABEQ) are not enough to remove the systematic effect. Moreover, due to the narrow FOV of the HRSI, position and altitude are highly correlated making it almost impossible to separate and remove their systematic effects without extending the geometric model (Self-Calib.) The systematic effects gets evident on the increase of accuracy (in terms of RMSE at GCPs) for looser and relaxed ground control at the expense of large and strong block deformation with large residuals at check points. Systematic errors are most freely distributed and their effects propagated all over the block.

Passini, Richardo M.↗
NASA NTRS2012

Inferring Lower Boundary Driving Conditions Using Vector Magnetic Field Observations

Low-beta coronal MHD simulations of realistic CME events require the detailed specification of the magnetic fields, velocities, densities, temperatures, etc., in the low corona. Presently, the most accurate estimates of solar vector magnetic fields are made in the high-beta photosphere. Several techniques have been developed that provide accurate estimates of the associated photospheric plasma velocities such as the Differential Affine Velocity Estimator for Vector Magnetograms and the Poloidal/Toroidal Decomposition. Nominally, these velocities are consistent with the evolution of the radial magnetic field. To evolve the tangential magnetic field radial gradients must be specified. In addition to estimating the photospheric vector magnetic and velocity fields, a further challenge involves incorporating these fields into an MHD simulation. The simulation boundary must be driven, consistent with the numerical boundary equations, with the goal of accurately reproducing the observed magnetic fields and estimated velocities at some height within the simulation. Even if this goal is achieved, many unanswered questions remain. How can the photospheric magnetic fields and velocities be propagated to the low corona through the transition region? At what cadence must we observe the photosphere to realistically simulate the corona? How do we model the magnetic fields and plasma velocities in the quiet Sun? How sensitive are the solutions to other unknowns that must be specified, such as the global solar magnetic field, and the photospheric temperature and density?

Schuck, Peter W.↗
DOE OSTI2023

VPS26 Moonlights as an Arrestin-like Adapter for a 7-transmembrane RGS 2 protein in Arabidopsis thaliana

Extracellular signals perceived by 7-transmembrane (7TM)-spanning receptors on the plasma membrane utilize a cytoplasmic adaptor that propagates signaling and initiates a feedback circuit for desensitization by removal of these receptors at the plasma membrane leading to desensitization to that signal. Signal (agonist) binding often evokes phosphorylation at the C-terminal tail of many 7TM G-protein-coupled receptors in animal cells which then recruits a cytoplasmic intermediate adaptor, β-arrestin, that sets in motion clathrin-mediated endocytosis (CME). Some 7TM receptors have agonist-induced phosphorylation and CME that do not involve β-arrestin, therefore it is unclear how these phosphorylated 7TM receptors are internalized. Arrestins, neither α– or β-types, are not encoded in the Arabidopsis genome, yet Arabidopsis cells have a well characterized signal-induced CME of a 7TM protein, Regulator of G Signaling 1 (AtRGS1). We show that a component of the retromer complex, VPS26, moonlights as an arrestin-like adapter. VPS26A and VPS26B form heterodimer instead of homodimer or monomer. flg22, a bacterium-derived signal that alerts plant cells of the presence of a potential pathogen, induces phosphorylation of AtRGS1 within a cluster of serines in its C-terminal tail, dimerization, and its CME. The phosphorylated C-terminal tail peptide binds to VPS26A/B heterodimer with greater affinity than the unphosphorylated peptide. We propose that VPS26 serves as an arrestin-like adaptor in the CME of AtRGS1 dimerization biased for VPS26 signaling.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2002

Pulse Detonation Rocket MHD Power Experiment

A pulse detonation research engine (MSFC (Marshall Space Flight Center) Model PDRE (Pulse Detonation Rocket Engine) G-2) has been developed for the purpose of examining integrated propulsion and magnetohydrodynamic power generation applications. The engine is based on a rectangular cross-section tube coupled to a converging-diverging nozzle, which is in turn attached to a segmented Faraday channel. As part of the shakedown testing activity, the pressure wave was interrogated along the length of the engine while running on hydrogen/oxygen propellants. Rapid transition to detonation wave propagation was insured through the use of a short Schelkin spiral near the head of the engine. The measured detonation wave velocities were in excess of 2500 m/s in agreement with the theoretical C-J velocity. The engine was first tested in a straight tube configuration without a nozzle, and the time resolved thrust was measured simultaneously with the head-end pressure. Similar measurements were made with the converging-diverging nozzle attached. The time correlation of the thrust and head-end pressure data was found to be excellent. The major purpose of the converging-diverging nozzle was to configure the engine for driving an MHD generator for the direct production of electrical power. Additional tests were therefore necessary in which seed (cesium-hydroxide dissolved in methanol) was directly injected into the engine as a spray. The exhaust plume was then interrogated with a microwave interferometer in an attempt to characterize the plasma conditions, and emission spectroscopy measurements were also acquired. Data reduction efforts indicate that the plasma exhaust is very highly ionized, although there is some uncertainty at this time as to the relative abundance of negative OH ions. The emission spectroscopy data provided some indication of the species in the exhaust as well as a measurement of temperature. A 24-electrode-pair segmented Faraday channel and 0.6 Tesla permanent magnet assembly were then installed on Marshall Space Flight Center's (MSFC's) rectangular channel pulse detonation research engine. Magnetohydrodynamic (MHD) electrical power extraction experiments were carried out for a range of load impedances in which cesium hydroxide seed (dissolved in methanol) was sprayed into the gaseous oxygen/hydrogen propellants. Positive power extraction was obtained, but preliminary analysis of the data indicated that the plasma electrical conductivity is lower than anticipated and the near-electrode voltage drop is not negligible. It is believed that the electrical conductivity is reduced due to a large population of negative OH ions. This occurs because OH has a strong affinity for capturing free electrons. The effect of near-electrode voltage drop is associated with the high surface-to-volume ratio of the channel (1-inch by 1-inch cross-section) where surface effects play a dominant role. As usual for MHD devices, higher performance will require larger scale devices. Overall, the gathered data is extremely valuable from the standpoint of understanding plasma behavior and for developing empirical scaling laws.

Litchford, Ron J.↗