Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “IDENTIFICATION”

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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Direct identification of Ac and No molecules with an atom-at-a-time technique

The periodic table provides an intuitive framework for understanding chemical properties. However, its traditional patterns may break down for the heaviest elements occupying the bottom of the chart. Here, the large nuclei of actinides (Z > 88) and superheavy elements (Z ≥ 104) give rise to relativistic effects that are expected to substantially alter their chemical behaviours, potentially indicating that we have reached the end of a predictive periodic table. Relativistic effects have already been cited for the unusual chemistry of the actinides compared with those of their lanthanide counterparts. Unfortunately, it is difficult to understand the full impact of relativistic effects, as research on the later actinides and superheavy elements is scarce. Beyond fermium (Z = 100), elements need to be produced and studied one atom at a time, using accelerated ion beams and state-of-the-art experimental approaches. So far, no experiments have been capable of directly identifying produced molecular species. Here ions of actinium (Ac, Z = 89) and nobelium (No, Z = 102) were synthesized through nuclear reactions at the 88-Inch Cyclotron facility at Lawrence Berkeley National Laboratory and then exposed to trace amounts of H 2 O and N 2 . The produced molecular species were directly identified by measuring their mass-to-charge ratios using FIONA (For the Identification Of Nuclide A). These results mark the first, to our knowledge, direct identification of heavy-element molecular species using an atom-at-a-time technique and highlight the importance of such identifications in future superheavy-element chemistry experiments to deepen understanding of their chemical properties.

Pore, Jennifer L. [Lawrence Berkeley National Labo↗
DOE OSTI2020

Expectation-propagation for weak radionuclide identification at radiation portal monitors

We propose a sparsity-promoting Bayesian algorithm capable of identifying radionuclide signatures from weak sources in the presence of a high radiation background. The proposed method is relevant to radiation identification for security applications. In such scenarios, the background typically consists of terrestrial, cosmic, and cosmogenic radiation that may cause false positive responses. We evaluate the new Bayesian approach using gamma-ray data and are able to identify weapons-grade plutonium, masked by naturally-occurring radioactive material (NORM), in a measurement time of a few seconds. We demonstrate this identification capability using organic scintillators (stilbene crystals and EJ-309 liquid scintillators), which do not provide direct, high-resolution, source spectroscopic information. Compared to the EJ-309 detector, the stilbene-based detector exhibits a lower identification error, on average, owing to its better energy resolution. Organic scintillators are used within radiation portal monitors to detect gamma rays emitted from conveyances crossing ports of entry. The described method is therefore applicable to radiation portal monitors deployed in the field and could improve their threat discrimination capability by minimizing “nuisance” alarms produced either by NORM-bearing materials found in shipped cargoes, such as ceramics and fertilizers, or radionuclides in recently treated nuclear medicine patients.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2019

A generalized deep learning approach for local structure identification in molecular simulations

Identifying local structure in molecular simulations is of utmost importance. The most common existing approach to identify local structure is to calculate some geometrical quantity referred to as an order parameter. In simple cases order parameters are physically intuitive and trivial to develop (e.g., ion-pair distance), however in most cases, order parameter development becomes a much more difficult endeavor (e.g., crystal structure identification). Using ideas from computer vision, we adapt a specific type of neural network called a PointNet to identify local structural environments in molecular simulations. A primary challenge in applying machine learning techniques to simulation is selecting the appropriate input features. This challenge is system-specific and requires significant human input and intuition. In contrast, our approach is a generic framework that requires no system-specific feature engineering and operates on the raw output of the simulations, i.e., atomic positions. We demonstrate the method on crystal structure identification in Lennard-Jones (four different phases), water (eight different phases), and mesophase (six different phases) systems. The method achieves as high as 99.5% accuracy in crystal structure identification. The method is applicable to heterogeneous nucleation and it can even predict the crystal phases of atoms near external interfaces. We demonstrate the versatility of our approach by using our method to identify surface hydrophobicity based solely upon positions and orientations of surrounding water molecules. Our results suggest the approach will be broadly applicable to many types of local structure in simulations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Geolocation tracking for human identification and activity recognition using radar deep transfer learning

Abstract Human identification and activity recognition (HIAR) is crucial for many applications, such as surveillance, smart homes, and assisted living. As a sensing modality, radar has many unique characteristics including privacy protection, and contactless sensing. Single classification systems have shown to be accurate, but for long‐term solutions both human identification (ID) and human activity recognition (HAR) will need to be integrated in one system where it can be utilised simultaneously. In this article, a novel radar‐based human tracking system is presented where three classifiers are utilised to identify the subject and his/her behaviour. For any kind of motion, the system tracks the subject and detect the type of his/her motion. Based on the detected type of motion, the three classifiers are utilised for identification and activity recognition. The classifiers are built utilising deep transfer learning where three radar datasets are established to train and validate each of the deep networks. To recognise six activities and 10 human subjects, the three classifiers, namely, HAR, Gait ID, and Heart sound ID, achieve superior performance compared to the best reported results in literature with classification accuracies of 97.6%, 100%, and 41.8% respectively. Three successful examples are presented to demonstrate the introduced concept.

Alkasimi, Ahmad↗
DOE OSTI2015

Submillimeter laboratory identification of CH{sup +} and CH{sub 2}D{sup +}

Laboratory identification of two basic and important interstellar molecular ions is presented. The J = 1 - 0 rotational transition of {sup 12}CH{sup +} together with those of {sup 13}CH{sup +} and {sup 12}CD{sup +} was observed in the laboratory. The newly obtained frequencies were found to be different from those reported previously. Various experimental evidences firmly support the new measurements. In addition, the Zeeman effect and the spin-rotation hyperfine interaction enforce the laboratory identification with no ambiguity. Rotational lines of CH{sub 2}D{sup +} were observed in the submillimeter-wave region. This laboratory observation is consistent with a recent tentative identification of CH{sub 2}D{sup +} toward Ori IRc2.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2015

Use of dc Ar microdischarge with nonlocal plasma for identification of metal samples

The possibility of using the collisional electron spectroscopy (CES) method for the detection of atoms from metal samples is experimentally verified. The detection and identification of metal atoms from a Pt sample in the nonlocal plasma of short (without positive column) dc Ar microdischarge at intermediate pressures (5–30 Torr) is realized in this work. Cathode sputtering is used for atomization of the metal under analysis. The identification of the analyzed metal is made from the energy spectra of groups of fast nonlocal electrons—characteristic electrons released in the Penning ionization of the Pt atoms by Ar metastable atoms and molecules. The acquisition of the electron energy spectra is performed using an additional electrode—a sensor located at the boundary of the discharge volume. The Pt characteristic Penning electrons form the maxima in the electron energy spectra at the energies of their appearance, which are 2.6 eV and 1.4 eV. From the measured energy of the maxima, identification of the metal atoms is accomplished. The characteristic Ar maxima due to pair collisions between Ar metastable atoms and molecules and super-elastic collisions are also recorded. This study demonstrates the possibility of creating a novel microplasma analyzer for atoms from metal samples.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Identification of Blue Horizontal Branch Stars with Multimodal Fusion

Blue Horizontal Branch stars (BHBs) are ideal tracers to probe the global structure of the milky Way (MW), and the increased size of the BHB star sample could be helpful to accurately calculate the MW's enclosed mass and kinematics. Large survey telescopes have produced an increasing number of astronomical images and spectra. However, traditional methods of identifying BHBs are limited in dealing with the large scale of astronomical data. A fast and efficient way of identifying BHBs can provide a more significant sample for further analysis and research. Therefore, in order to fully use the various data observed and further improve the identification accuracy of BHBs, we have innovatively proposed and implemented a Bi-level attention mechanism-based Transformer multimodal fusion model, called Bi-level Attention in the Transformer with Multimodality (BATMM). The model consists of a spectrum encoder, an image encoder, and a Transformer multimodal fusion module. The Transformer enables the effective fusion of data from two modalities, namely image and spectrum, by using the proposed Bi-level attention mechanism, including cross-attention and self-attention. As a result, the information from the different modalities complements each other, thus improving the accuracy of the identification of BHBs. The experimental results show that the F1 score of the proposed BATMM is 94.78%, which is 21.77% and 2.76% higher than the image and spectral unimodality, respectively. It is therefore demonstrated that higher identification accuracy of BHBs can be achieved by means of using data from multiple modalities and employing an efficient data fusion strategy.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Comparison of Rotor Wake Identification and Characterization Methods for the Analysis of Wake Dynamics and Evolution

Optimal wind power plant design requires understanding of wind turbine wake physics and validation of engineering wake models under wake-controlled operating conditions. In this work, we have developed and investigated several different wake identification and characterization methods for analyzing wake evolution and dynamics. The accuracy and robustness of these methods, based on Gaussian function fitting and adaptive contour identification, have been assessed by application to a large-eddy simulation data set. A new contour-based method based on downstream momentum deficit has been considered. Uncertainties arising from wake-identification errors result in characterizations of the wake expansion, recovery, and meandering motion that differ by 19% of the rotor area, 4% of the freestream, and 15% rotor diameter, respectively.

17 WIND ENERGY↗
DOE OSTI2026

Calibrated particle identification for Belle II

We present several efforts aimed at improving charged particle identification at the Belle II experiment. We define an ablation test to quantify and evaluate the impact of each sub-detector on the global particle identification performance. We demonstrate that the performance of the identification scheme can be improved via a simple calibration of the sub-detector likelihoods through a set of per hypothesis, per sub-detector weights. Finally, we present preliminary results on an improved definition of the likelihoods contributed by the electromagnetic calorimeter. A set of multiclass boosted decision trees is trained to exploit the shape of energy depositions of different particle species. In simulated $B\bar{B}$ events, the pion-to-electron and muon fake rates are reduced by 55% and 31% respectively at low-medium momentum.

Hohmann, Marcel [Univ. of Melbourne, Parkville, VI↗
DOE OSTI2017

The Convolutional Visual Network for Identification and Reconstruction of NOvA Events

In 2016 the NOvA experiment released results for the observation of oscillations in the vμ and ve channels as well as ve cross section measurements using neutrinos from Fermilab’s NuMI beam. These and other measurements in progress rely on the accurate identification and reconstruction of the neutrino flavor and energy recorded by our detectors. This presentation describes the first application of convolutional neural network technology for event identification and reconstruction in particle detectors like NOvA. The Convolutional Visual Network (CVN) Algorithm was developed for identification, categorization, and reconstruction of NOvA events. It increased the selection efficiency of the ve appearance signal by 40% and studies show potential impact to the vμ disappearance analysis.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2016

A mass graph-based approach for the identification of modified proteoforms using top-down tandem mass spectra

Motivation: Although proteomics has rapidly developed in the past decade, researchers are still in the early stage of exploring the world of complex proteoforms, which are protein products with various primary structure alterations resulting from gene mutations, alternative splicing, post-translational modifications, and other biological processes. Proteoform identification is essential to mapping proteoforms to their biological functions as well as discovering novel proteoforms and new protein functions. Top-down mass spectrometry is the method of choice for identifying complex proteoforms because it provides a “bird’s eye view” of intact proteoforms. The combinatorial explosion of various alterations on a protein may result in billions of possible proteoforms, making proteoform identification a challenging computational problem. Results: We propose a new data structure, called the mass graph, for efficient representation of proteoforms and design mass graph alignment algorithms. We developed TopMG, a mass graph-based software tool for proteoform identification by top-down mass spectrometry. Experiments on top-down mass spectrometry data sets showed that TopMG outperformed existing methods in identifying complex proteoforms.

Dynamic Programming↗
DOE OSTI2023

Moment tensor event identification for collapses

SUMMARY We introduce a seismic identification method for collapse events using moment tensors (MTs). We start by computing full (six-element) MT solutions for 43 identified collapse events from around the world, and statistically characterizing the population on the MT hypersphere. We then test a large data set of over 1000 full MTs for the western U.S. against the distribution of collapses using a MT-based identification method similarly as used for testing explosions. Known collapses and explosions are readily identified, along with other anomalous events in the Geysers and central California coast. Misidentification rates are determined for various screening angles with optimal misidentification rates between earthquakes and collapses on the order of 3 per cent. The method is demonstrated to be very effective at identifying non-earthquake sources with a 97–98 per cent accuracy. It is likely to be transportable to other regions, and can be used for event identification anywhere full MT solutions are routinely calculated.

58 GEOSCIENCES↗
DOE OSTI2015

A Generic Isotope Identification Approach for nuclear instrumentation

Isotope identification is generally done from spectra from high intrinsic resolution such as germanium. A lot of approaches are proposed in the literature. Most of them are not efficient with poor energy resolution detectors such as non-loaded scintillators. The proposed approach is a new isotope identification principle to deal with the overall range of nuclear detectors. The proposed method allows the identification of isotopes using detectors with poor intrinsic resolution and lower counting than the existing techniques. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Online Dynamic Mode Decomposition Based System Identification of Multi-Zone Building HVAC Systems

Many works have recently been conducted to reduce the electricity consumption of smart buildings and allow them to support various grid services. Most of these works require accurate system models for the various appliances in the building including heating, ventilation, and air conditioning (HVAC) units. In this paper, we investigate a recursive data-driven system identification strategy to construct the thermal model for a time-varying building with a multi-zone HVAC unit. The online dynamic mode decomposition (DMD)-based strategy is employed to identify the multi-zone thermal building dynamics, where a simple information update (rank-1) is selected to avoid computational complexity. The DMD-based identification strategy is validated using a real gymnasium building equipped with a 4-zone HVAC unit, and its performance is compared with that of the traditional nuclear-norm subspace identification (N2SID) strategy.

Wu, Tumin [University of Tennessee, Knoxville (UTK↗
DOE OSTI2021

Identification of a Delay Attack in the Secondary Control of Grid-Tied Inverter Systems

This work is developed for the identification of a denial-of-service cyberattack on the secondary controller of inverter systems which is connected to the power grid. The identification is made through the dynamic response of the reactive power (Q) of the system under attack. By observing the dynamic characteristic of Q, it is possible to correlate the attack with the nominal response of the hierarchical controller. The article shows that the occurrence of the attack can be identified through a supervisory control that runs a model in parallel. The article argues that after an early identification of the attack, a local controller can take action to mitigate its effects on the system’s response.

Roig Greidanus, Mateo D.↗
DOE OSTI2025

Identification Uncertainty in Inverse Material Model Parameter Determination: A Sensitivity‐Based Decision Process for Load Path Selection

This research proposes a sensitivity-based framework for selecting the optimal prescribed loading path for a biaxial cruciform specimen. Optimality here is determined by the direction and magnitude of the prescribed displacement that minimizes the influence of random noise on the material model parameter identification. Using simulated experimental data based on finite element simulation, in this work, we identify the material model parameters of a Ludwik hardening model and plane stress implementation of the Hill-48 yield criterion using finite element model updating (FEMU). Our analysis reveals that the identification (or estimator) uncertainty of model parameters depends on the displacement boundary conditions (i.e., loading sequence) and the ground-truth value of the individual parameters. Optimal experimental design (OED) criteria based on the Fisher information matrix were investigated to mitigate indecision in the choice of optimal load path when the identification uncertainty of different material model parameters optimized at different load paths. The determinant of the Fisher information matrix was chosen here as the more useful metric due to its ability to capture uncertainty of the most influential material model parameters. The proposed framework demonstrates potential for real-time automated load step selection using scalar criteria derived prior to mechanical loading. The framework can be generalized to other geometries, boundary conditions and material models, allowing this procedure to be utilized for different experimental configurations and materials.

Fayad, Samuel S. [University of Illinois at Urbana↗
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 OSTI2015

SU-E-I-74: Image-Matching Technique of Computed Tomography Images for Personal Identification: A Preliminary Study Using Anthropomorphic Chest Phantoms

Purpose: Fingerprints, dental impressions, and DNA are used to identify unidentified bodies in forensic medicine. Cranial Computed tomography (CT) images and/or dental radiographs are also used for identification. Radiological identification is important, particularly in the absence of comparative fingerprints, dental impressions, and DNA samples. The development of an automated radiological identification system for unidentified bodies is desirable. We investigated the potential usefulness of bone structure for matching chest CT images. Methods: CT images of three anthropomorphic chest phantoms were obtained on different days in various settings. One of the phantoms was assumed to be an unidentified body. The bone image and the bone image with soft tissue (BST image) were extracted from the CT images. To examine the usefulness of the bone image and/or the BST image, the similarities between the two-dimensional (2D) or threedimensional (3D) images of the same and different phantoms were evaluated in terms of the normalized cross-correlation value (NCC). Results: For the 2D and 3D BST images, the NCCs obtained from the same phantom assumed to be an unidentified body (2D, 0.99; 3D, 0.93) were higher than those for the different phantoms (2D, 0.95 and 0.91; 3D, 0.89 and 0.80). The NCCs for the same phantom (2D, 0.95; 3D, 0.88) were greater compared to those of the different phantoms (2D, 0.61 and 0.25; 3D, 0.23 and 0.10) for the bone image. The difference in the NCCs between the same and different phantoms tended to be larger for the bone images than for the BST images. These findings suggest that the image-matching technique is more useful when utilizing the bone image than when utilizing the BST image to identify different people. Conclusion: This preliminary study indicated that evaluating the similarity of bone structure in 2D and 3D images is potentially useful for identifying of an unidentified body.

60 APPLIED LIFE SCIENCES↗