Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Classification”

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

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Altermagnetism classification

Altermagnets are magnetic states with fully compensated spins and broken PT (PT: parity times time reversal) symmetry (i.e., spin-split bands). We classify three kinds of altermagnets in terms of broken P and T. Furthermore, strong altermagnets have spin-split bands without spin-orbit coupling (SOC), and weak altermagnets has spin-split bands only with non-zero SOC. These strong vs. weak altermagnets can be identified from the total number of symmetric spin rotation operations.

Altermagnets↗
DOE OSTI2026

Plasma confinement state classification in fusion power plants: Profile reflectometer and ensemble diagnostics

As Fusion Pilot Plants (FPPs) are increasingly viewed as within reach, many engineering challenges remain. Not many diagnostics are expected to be available in a reactor environment. Survivability, maintainability, and limited port space substantially restrict the number of FPP-relevant diagnostics. One remaining challenge is developing tools and devices to extract plasma state information necessary for controlling an FPP from a limited subset of diagnostics. This work is part of an overarching project to address this challenge. The specific diagnostic subset to be used in FPPs is still under debate. We take the approach of developing machine-learning-based tools for different significant plasma state parameters, using already known FPP-viable diagnostics. Previously we developed a plasma confinement mode classifier utilizing the Electron Cyclotron Emission (ECE) diagnostic. Here, we expand on this by developing a Profile Reflectometer (PR) based classifier with 97% test accuracy, and an ensemble model that combines the ECE and PR models into a single model, achieving 99% test accuracy.

Clark, Randall [Univ. of California, San Diego, CA↗
DOE OSTI2024

Photon classification with Gradient Boosted Trees at CLAS12

Dihadron semi-inclusive deep inelastic scattering (SIDIS) of 10.6 GeV longitudinally polarized electrons off the proton has been measured using the CLAS12 detector at Jefferson Lab. Two separate channels, π + π 0 and π - π 0 , were analyzed, requiring the reconstruction of diphoton pairs. Here, in this analysis, we addressed the problem of false neutral particles being reconstructed by CLAS12's event builder, polluting the otherwise physical combinatorial background underneath the π 0 peak. A photon classifier using a Gradient Boosted Trees (GBTs) architecture was trained with Monte Carlo simulations to reduce the amount of background π 0 's. We show that the nearest-neighbor features learned by the model lead to a substantial increase in signal vs. background discrimination compared to previous CLAS12 π^0 analyses. The machine learning approach recovers several times more dihadron statistics for the dataset.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Automating galaxy morphology classification using k -nearest neighbours and non-parametric statistics

ABSTRACT Morphology is a fundamental property of any galaxy population. It is a major indicator of the physical processes that drive galaxy evolution and in turn the evolution of the entire Universe. Historically, galaxy images were visually classified by trained experts. However, in the era of big data, more efficient techniques are required. In this work, we present a k-nearest neighbours based approach that utilizes non-parametric morphological quantities to classify galaxy morphology in Sloan Digital Sky Survey images. Most previous studies used only a handful of morphological parameters to identify galaxy types. In contrast, we explore 1023 morphological spaces (defined by up to 10 non-parametric statistics) to find the best combination of morphological parameters. Additionally, while most previous studies broadly classified galaxies into early types and late types or ellipticals, spirals, and irregular galaxies, we classify galaxies into 11 morphological types with an average accuracy of ${\sim} 80\!-\!90 \, {{\rm per\, cent}}$ per T-type. Our method is simple, easy to implement, and is robust to varying sizes and compositions of the training and test samples. Preliminary results on the performance of our technique on deeper images from the Hyper Suprime-Cam Subaru Strategic Survey reveal that an extension of our method to modern surveys with better imaging capabilities might be possible.

Mukundan, Kavya↗
DOE OSTI2024

Classification of electronic nematicity in three-dimensional crystals and quasicrystals

Electronic nematic order has been reported in a rich landscape of materials, encompassing not only a range of intertwined correlated and topological phenomena but also different underlying lattice symmetries. Motivated by these findings, we investigate the behavior of electronic nematicity as the spherical symmetry of three-dimensional (3D) space is systematically lowered by the lattice environment. Here, we consider all 32 crystallographic point groups as well as four major classes of quasicrystalline point groups, given the recent observations of electronic phases of interest in quasicrystalline materials and artificial twisted quasicrystals. Valuable insights are gained by establishing a mapping between the five-component charge-quadrupolar nematic order parameter of the electronic fluid and the 3D tensorial order parameter of nematic liquid crystals. We find that a uniaxial nematic state is only generically realized in polyhedral point groups (icosahedral and cubic), with the nematic director pointing along different sets of rotational symmetry axes. Interestingly, icosahedral point groups are the only ones in which the five nematic order parameter components transform as the same irreducible representation, making them the closest analog of 3D isotropic nematics. In axial point groups, one of the nematic components is always condensed, whereas the other four components decompose into an in-plane and an out-of-plane nematic doublet, resulting in biaxial nematic ground states. Because these two nematic doublets behave as Z q clock order parameters, this allows us to identify the types of crystals and quasicrystals that can host interesting electronic nematic phenomena enabled by the critical properties of the q ≥ 4 clock model, such as emergent continuous nematic fluctuations in 3D, critical phases with quasi-long-range nematic order in two dimensions (2D), and Ashkin-Teller nematicity in 2D.

3-dimensional systems↗
DOE OSTI2024

Classification of compact objects and model comparison using EOS knowledge

Nuclear theory and experiments, alongside astrophysical observations, constrain the equation of state (EOS) of supranuclear-dense matter. Conversely, knowledge of the EOS allows an improved interpretation of nuclear or astrophysical data. In this article, we use several established constraints on the EOS and the new NICER measurement of PSR J0437-4715 to comment on the nature of the primary companion in GW230529 and the companion of PSR J0514-4002E. We find that, with a probability of ≳84% and ≳68%, respectively, both objects are black holes. These likelihoods increase to above 95% when one uses GW170817’s remnant as an upper limit on the TOV mass. We also demonstrate that the current knowledge of the EOS substantially disfavors high masses and radii for PSR J⁢0030+0451, inferred recently when combining NICER with XMM-Newton background data and using particular hot-spot models. Lastly, we also use our obtained EOS knowledge to comment on measurements of the nuclear symmetry energy, finding that the large value predicted by the PREX-II measurement displays some mild tension with other constraints on the EOS.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Designing Remote Monitoring for Smart Manufacturing Facilities: Hazard Identification and Classification

This study investigates the process of hazard identification in complex manufacturing environments during the design phase, emphasizing the significance of the design process in developing designs that effectively mitigate hazards in contexts with numerous variables, such as a variety of machines, sensors, actuators, and agents. Through a mixed-methods approach, the objective of this work is to understand how the evolution of design outcomes across various stages might influence a designer’s ability to recognize both standard and novel hazards. To achieve this understanding, an experimental design task was conducted with six designers from a national lab specializing in manufacturing technologies. This approach combined qualitative and quantitative data analysis from a one-hour virtual session with participants. Findings suggest that the complexity of identifying hazards in a high-dimensional design space is challenging within a limited time frame and that the identification of hazards is significantly influenced by the stage of the design task and the initial design decisions, indicating the need for extended time and strategic initial planning in the design process to enhance hazard identification.

Ballestas, Caseysimone↗
DOE OSTI2024

A Compound Data Poisoning Technique with Significant Adversarial Effects on Transformer-based Sentiment Classification Tasks

Transformer-based models have demonstrated much success in various natural language processing tasks. However, they are often vulnerable to adversarial attacks, such as data poisoning, which can intentionally fool the model into generating incorrect results. In this article, we present a novel, compound variant of a data poisoning attack on a transformer-based model that maximizes the poisoning effect while minimizing the scope of poisoning. Here we do so by combining the established data poisoning technique (label flipping) with a novel adversarial artifact selection and insertion technique aimed at minimizing detectability and the scope of the poisoning footprint. We find that by using a combination of these two techniques, we achieve a state-of-the-art attack success rate of approximately 90% while poisoning only 0.5% of the original training set, thus minimizing the scope and detectability of the poisoning action. These findings have the potential to advance the development of better data poisoning detection methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Data-Driven Clustering and Classification of Outage Patterns with Insights into their Links to Extreme Events

At a global level extreme events have increased in both scale and impact. These events have the potential to affect the electrical grid infrastructure and cause a wide range of outages, which can lead to a disruption in daily patterns, cost millions of dollars and also the loss of life. Currently, to track these outage events there have been various approaches developed ranging from regional to national level quantifications for what defines an outage. However, this variation in methods can potentially lead to subjective decision-making and a lack of proper management in relation to the event. While previous work has made strides in determining spatio-temporal patterns, minimal attention has been given to the type and number of outages an area may be exposed to. The differences in incurred cost and the overall severity of an event between a transformer box malfunction and a hurricane are drastic, and by finding historical signals, we can allow for more efficient management, potentially saving lives and millions of dollars. Here, we leverage unsupervised machine learning techniques to delineate outage patterns among 22 counties within the United States and find that there are clear, segregated clusters (0.93 silhouette) of data which are related by event behavior and underlying cause. This finding will allow for energy stakeholders, policy makers, and researchers to gain a deeper understanding of the extent and severity of historic events and to better prepare for electrical grid infrastructure planning and management.

Koob, Benjamin [ORNL]↗
DOE OSTI2026

Hyperdimensional computing for image classification (HDC) v1.0

This is an implementation of the hyperdimensional computing technique to classify images. It consists of a python script that trains the system for a set of images from a set of images (dataset) specified by the user. This training produces hardware configuration parameters and description vectors that are then loaded into the hardware description part of the project. The hardware description consists of hardware described in Verilog (a well known language for this purpose) that is synthesizable and can be implemented in a real chip. This hardware received the training information generated by python, and then is able to accept images to produce answers for each image on which category (class) from the pre-=trained ones the image belongs to. The hardware and python training scripts are configurable and documented. The advantage of hyperdimensional computing is its robustness to errors and the easy capability for online learning (refining the training during inference slowly over time), which this implementation supports.

Michelogiannakis, Georgios [Lawrence Berkeley Nati↗
DOE OSTI2025

GRinding Automated Classification Engine

This work is an ML-driven framework for automated surface analysis of microscopy images. We create a training dataset by imaging stainless steel samples to benchmark four developed deep neural network architectures. These models, based on a YOLOv8n-cls backend, integrate image features and process metadata using various fusion methods to distinguish between acceptable and unacceptable surface finishes. This code is associated with publication "Classifying Alloy Surface Preparation Quality with Metadata-Infused Machine Learning for Rapid Alloy Discovery" for project APEX LDRD-ER (25-ERD-039)

Gongora, AldairE [Lawrence Livermore National Labo↗