Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “discrimination”

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 1,801 records · Page 100
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

End-to-End Physics Event Classification with CMS Open Data: Applying Image-Based Deep Learning to Detector Data for the Direct Classification of Collision Events at the LHC

This paper describes the construction of novel end-to-end image-based classifiers that directly leverage low-level simulated detector data to discriminate signal and background processes in pp collision events at the Large Hadron Collider at CERN. To better understand what end-to-end classifiers are capable of learning from the data and to address a number of associated challenges, we distinguish the decay of the standard model Higgs boson into two photons from its leading background sources using high-fidelity simulated CMS Open Data. We demonstrate the ability of end-to-end classifiers to learn from the angular distribution of the photons recorded as electromagnetic showers, their intrinsic shapes, and the energy of their constituent hits, even when the underlying particles are not fully resolved, delivering a clear advantage in such cases over purely kinematics-based classifiers.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Quantum-assisted associative adversarial network: applying quantum annealing in deep learning

Abstract Generative models have the capacity to model and generate new examples from a dataset and have an increasingly diverse set of applications driven by commercial and academic interest. In this work, we present an algorithm for learning a latent variable generative model via generative adversarial learning where the canonical uniform noise input is replaced by samples from a graphical model. This graphical model is learned by a Boltzmann machine which learns low-dimensional feature representation of data extracted by the discriminator. A quantum processor can be used to sample from the model to train the Boltzmann machine. This novel hybrid quantum-classical algorithm joins a growing family of algorithms that use a quantum processor sampling subroutine in deep learning, and provides a scalable framework to test the advantages of quantum-assisted learning. For the latent space model, fully connected, symmetric bipartite and Chimera graph topologies are compared on a reduced stochastically binarized MNIST dataset, for both classical and quantum sampling methods. The quantum-assisted associative adversarial network successfully learns a generative model of the MNIST dataset for all topologies. Evaluated using the Fréchet inception distance and inception score, the quantum and classical versions of the algorithm are found to have equivalent performance for learning an implicit generative model of the MNIST dataset. Classical sampling is used to demonstrate the algorithm on the LSUN bedrooms dataset, indicating scalability to larger and color datasets. Though the quantum processor used here is a quantum annealer, the algorithm is general enough such that any quantum processor, such as gate model quantum computers, may be substituted as a sampler.

Wilson, Max (ORCID:0000000207983391)↗
DOE OSTI2020

On the competition between mixing rate and uniformity in a coaxial hydrodynamic focusing mixer

Fast microfluidic mixers for use with line-of-sight integrating detection schemes pose unique challenges. Such detectors typically cannot discriminate signal from slow moving (e.g. near internal walls) and fast-moving portions of the fluid stream. This convolves reaction rate dynamics with fluid flow residence time dynamics. Further, the small cross sections of typical three-dimensional hydrodynamic focusing devices lead to lower detection signals. The current study focuses on achieving both small time scales of mixing and homogenous residence times. This is achieved by injecting sample through a center capillary and hydrodynamically focusing using a sheath flow within a tapered second capillary. The current design also features a third, larger coaxial capillary. The mixed stream flows into the large cross-section of this third capillary to decelerate and expand the stream by up to 14-fold to improve line-of-sight signal strength of reaction products. Hydrodynamic focusing, mixing, and expansion are studied using analytical and numerical models and also studied experimentally using a fluorescein-iodide quenching reaction. The experimentally validated models are used to explore trade-offs between mixing rate and uniformity. For the first time, this work presents detailed analysis of the Lagrangian time history of species transport during mixing inside coaxial capillaries to measure mixing nonuniformity. The mixing region enables order 100 μs mixing times and residence time widths of the same order (140 μs).

42 ENGINEERING↗
DOE OSTI2015

Atomic-scale properties of Ni-based FCC ternary, and quaternary alloys

The aim of our study is to characterize some atomic-scale properties of Ni-based FCC multicomponent alloys. For this purpose, we use Monte Carlo method combined with density functional theory calculations to study short-range order (SRO), atomic displacements, electronic density of states, and magnetic moments in equimolar ternary NiCrCo, and quaternary NiCrCoFe alloys. The salient features for the ternary alloy are a negative SRO parameter between Ni Cr and a positive between Cr Cr pairs as well as a weakly magnetic state. For the quaternary alloy we predict negative SRO parameter for Ni Cr and Ni Fe pairs and positive for Cr Cr and Fe Fe pairs. Atomic displacements for both ternary and quaternary alloys are negligible. In contrast to the ternary, the quaternary alloy shows a complex magnetic structure. The electronic structure of the ternary and quaternary alloys shows differences near the Fermi energy between a random solid solution and the predicted structure with SRO. Despite that, the calculated EXAFS spectra does not show enough contrast to discriminate between random and ordered structures. Finally, the predicted SRO has an impact on point-defect energetics, electron phonon coupling and thermodynamic functions and thus, SRO should not be neglected when studying properties of these two alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Crowdsourced reliable labeling of safety-rule violations on images of complex construction scenes for advanced vision-based workplace safety

Construction workplace hazard detection requires engineers to analyze scenes manually against many safety rules, which is time-consuming, labor-intensive, and error-prone. Computer vision algorithms are yet to achieve reliable discrimination of anomalous and benign object relations underpinning safety violation detections. Recently developed deep learning-based computer vision algorithms need tens of thousands of images, including labels of the safety rules violated, in order to train deep-learning networks for acquiring spatiotemporal reasoning capacity in complex workplaces. Such training processes need human experts to label images and indicate whether the relationship between the worker, resource, and equipment in the scenes violate spatiotemporal arrangement rules for safe and productive operations. False alarms in those manual labels (labeling no-violation images as having violations) can significantly mislead the machine learning process and result in computer vision models that produce inaccurate hazard detections. Compared with false alarms, another type of mislabels, false negatives (labeling images having violations as “no violations”), seem to have fewer impacts on the reliability of the trained computer vision models.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Deep convolutional autoencoders as generic feature extractors in seismological applications

The idea of using a deep autoencoder to encode seismic waveform features and then use them in different seismological applications is appealing. In this paper, we designed tests to evaluate this idea of using autoencoders as feature extractors for different seismological applications, such as event discrimination (i.e., earthquake vs. noise waveforms, earthquake vs. explosion waveforms), and phase picking. These tests involve training an autoencoder, either undercomplete or overcomplete, on a large amount of earthquake waveforms, and then using the trained encoder as a feature extractor with subsequent application layers (either a fully connected layer, or a convolutional layer plus a fully connected layer) to make the decision. By comparing the performance of these newly designed models against the baseline models trained from scratch, we conclude that the autoencoder feature extractor approach may only outperform the baseline under certain conditions, such as when the target problems require features that are similar to the autoencoder encoded features, when a relatively small amount of training data is available, and when certain model structures and training strategies are utilized. The model structure that works best in all these tests is an overcomplete autoencoder with a convolutional layer and a fully connected layer to make the estimation.

58 GEOSCIENCES↗
DOE OSTI2022

Integrated Transcriptomic and Proteomic Analysis Identifies Plasma Biomarkers of Hepatocellular Failure in Alcohol-Associated Hepatitis

Alcohol-associated hepatitis (AH) is a form of liver failure with high short-term mortality. Recent results have shown that HNF4a defective function and systemic inflammation are major disease drivers of AH. Plasma biomarkers of hepatocyte function could be useful for diagnostic and prognostic purposes. Herein an integrative analysis of hepatic RNAseq and liquid chromatography-tandem mass spectrometry (LC-MS/MS) was performed to identify plasma protein signatures for mild and severe AH patients. Alcohol-related liver disease cirrhosis (ALD)(AC), non-alcoholic fatty liver disease (NALFD), and healthy subjects (HC) were used as comparator groups. Identified proteins primarily involved in hepatocellular function were decreased in AH patients which included hepatokines, clotting factors, complement cascade components, and hepatocyte growth activators. A protein signature of AH disease severity was identified including thrombin (THRB), hepatocyte growth factor alpha (HGFA), clusterin (CLUS), human serum factor H-related protein (FHR1) and kallistatin (KAIN), which exhibited large abundance shifts between severe and non-severe AH. The combination of THRB and HGFA discriminated between severe and non-severe AH with high sensitivity and specificity. These findings were correlated with the liver expression of genes encoding secreted proteins in a similar cohort, finding a highly consistent plasma protein signature reflecting HNF4A and HNF1A functions. This unbiased proteomic-transcriptome analysis identified plasma protein signatures and pathways associated with disease severity, reflecting HNF4A/1A activity useful for diagnostic assessment in AH.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Carbon use efficiency diagnostics in Nannochloropsis salina

We report that carbon dioxide (CO 2 ) can be a significant resource input affecting the cost of algae biomass production on an industrial scale. Improvements in biofuel productivity therefore require characterization of CO 2 use efficiency (CUE). RuBisCO saturation with CO 2 is an important factor influencing biomass productivity. During CO2 fixation by RuBisCO, fractionation of carbon isotopes occurs, with preferential fixation of 12 CO 2 , resulting in assimilation of the lighter isotope in algae. This photosynthetic discrimination (Δ DIC-algae ), approximated by the difference between the δ 13 C of external medium and that of algae, is a function of the proportion of CO 2 fixed relative to supply. Δ DIC-algae has been applied to the study of photosynthesis in algae over the past few decades and we have adopted the tool to characterize improvements in conditions optimized for biofuel productivity, such as controlled photobioreactors. We report the use of Δ DIC-algae as a tool for characterizing CO 2 dynamics and RuBisCO saturation in Nannochloropsis salina CCMP 1776, a benchmark strain in algal biofuels research. We use Δ DIC-algae to describe the conditions under which RuBisCO saturation can be achieved to maximize biomass productivity. Finally, our results suggest that determination of Δ DIC-algae can provide important feedback to support engineering and cultural improvements that can impact carbon use efficiency and biomass productivity.

09 BIOMASS FUELS↗
DOE OSTI2019

Calculation of the cross and auto power spectral densities for low neutron counting from pulse mode detectors

Here, this report introduces a methodology to calculate the prompt neutron decay constant of a subcritical assembly from the cross and auto power spectral densities of detectors operating in pulse mode when the neutron counting is low. The pulse operation mode allows the easy discrimination of the gamma contribution to the detector signal by the pulse height or shape techniques. The methodology is based on a MATLAB script that allows to change arbitrary parameters at any time after the experiment is performed. This technique requires list mode data and is much more flexible than programming the digital acquisition system because in the latter case the arbitrary parameters are set prior to the experiment and cannot be changed any longer after the experiment is performed. The MATLAB script processes all the detectors timestamps from several thousand pulses of the particle accelerator and redistributes them into a time interval that spreads over only 10 particle accelerator pulses. This pulses collapsing procedure has been performed because of the low neutron counting.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

A digital twin approach to system-level fault detection and diagnosis for improved equipment health monitoring

Automating the task of fault detection and diagnosis is crucial in the effort to reduce the operation and maintenance cost in the nuclear industry. This paper describes a physics-based approach for system-level diagnosis in thermal-hydraulic systems in nuclear power plants. The inclusion of physics information allows for the creation of virtual sensors, which provide improved fault diagnosis capability. The physics information also serves to better constrain diagnostic solutions to the physical domain. As a demonstration, various test cases for fault diagnosis in a high-pressure feedwater system were considered. The use of virtual sensors allows constructing performance models for two first-point feedwater heaters which would not have been possible otherwise due to the limited sensor set. Real-time plant data provided by a utility partner were used to assess the diagnostic approach. The detection of an abnormal event immediate after a plant startup pointed to faulty behaviors in the two first-point feedwater heaters. Further, this double-blind fault diagnosis was subsequently confirmed by the plant operator. In addition, several simulated sensor fault events demonstrated the capability of our algorithms in detecting and discriminating sensor faults.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2012

Conservation laws and stress-energy-momentum tensors for systems with background fields

This article attempts to delineate the roles played by non-dynamical background structures and Killing symmetries in the construction of stress-energy-momentum tensors generated from a diffeomorphism invariant action density. An intrinsic coordinate independent approach puts into perspective a number of spurious arguments that have historically lead to the main contenders, viz the Belinfante-Rosenfeld stress-energy-momentum tensor derived from a Noether current and the Einstein-Hilbert stress-energy-momentum tensor derived in the context of Einstein's theory of general relativity. Emphasis is placed on the role played by non-dynamical background (phenomenological) structures that discriminate between properties of these tensors particularly in the context of electrodynamics in media. These tensors are used to construct conservation laws in the presence of Killing Lie-symmetric background fields. - Highlights: Black-Right-Pointing-Pointer The role of background fields in diffeomorphism invariant actions is demonstrated. Black-Right-Pointing-Pointer Interrelations between different stress-energy-momentum tensors are emphasised. Black-Right-Pointing-Pointer The Abraham and Minkowski electromagnetic tensors are discussed in this context. Black-Right-Pointing-Pointer Conservation laws in the presence of nondynamic background fields are formulated. Black-Right-Pointing-Pointer The discussion is facilitated by the development of a new variational calculus.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2014

Phenomenological study of the interplay between IR-improved DGLAP-CS theory and the precision of an NLO ME matched parton shower MC

We present a phenomenological study of the current status of the application of our approach of exact amplitude-based resummation in quantum field theory to precision QCD calculations, by realistic MC event generator methods, as needed for precision LHC physics. We discuss recent results as they relate to the interplay of the attendant IR-improved DGLAP-CS theory of one of us and the precision of exact NLO matrix-element matched parton shower MC’s in the Herwig6.5 environment as determined by comparison to recent LHC experimental observations on single heavy gauge boson production and decay. The level of agreement between the new theory and the data continues to be a reason for optimism. In the spirit of completeness, we discuss as well other approaches to the same theoretical predictions that we make here from the standpoint of physical precision with an eye toward the (sub-)1% QCD⊗EW total theoretical precision regime for LHC physics. - Highlights: • Using LHC data, we show that IR-improved DGLAP-CS kernels with exact NLO Shower/ME matching improves MC precision. • We discuss other possible approaches in comparison with ours. • We propose experimental tests to discriminate between competing approaches.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Some results on the dynamics and transition probabilities for non self-adjoint hamiltonians

We discuss systematically several possible inequivalent ways to describe the dynamics and the transition probabilities of a quantum system when its hamiltonian is not self-adjoint. In order to simplify the treatment, we mainly restrict our analysis to finite dimensional Hilbert spaces. In particular, we propose some experiments which could discriminate between the various possibilities considered in the paper. An example taken from the literature is discussed in detail.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

Entanglement enhanced thermometry in the detection of the Unruh effect

We show how the use of entanglement can enhance the precision of the detection of the Unruh effect with an accelerated probe. We use a two-level atom interacting relativistically with a quantum field as the probe, and treat it as an open quantum system to derive the master equation governing its evolution. By means of quantum state discrimination, we detect the accelerated motion of the atom by examining its time evolving state. It turns out that the optimal strategy for the detection of the Unruh effect, to which the accelerated atom is sensitive, involves letting the atom-thermometer equilibrate with the thermal bath. However, introducing initial entanglement between the detector and an external degree of freedom leads to an enhancement of the sensitivity of the detector. Also, the maximum precision is attained within finite time, before equilibration takes place.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

Effect of the magnetic dipole interaction on a spin-1 system

We consider a hybrid system composed of a spin-1 triplet coupled to a nuclear spin. We study the effect of the axisymmetric and the quadrupole term of the magnetic dipole interaction between the two electrons forming the triplet on the energy spectrum in a static magnetic field. The energy spectrum obtained by directly diagonalizing the Hamiltonian of the system shows that these two terms not only remove the special crossings that appear in the absence of the magnetic dipole interaction, but also produce new (avoided) crossings by lifting the relevant levels. Specially, the gaps between the avoided crossing levels increase with the strength of the quadrupole term. In order to accurately illustrate these effects, we present the results for the discriminant and von Neumann entropy of one electron interacting with the rest of the whole system. Finally, by numerically solving the time-dependent Schrödinger equations of the system, we discover that the polarization oscillation of electron and nuclear spin is in-phase and the total average longitudinal spin is not conserved at location of avoided crossing, but the two results are opposite beyond that.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Atomic-scale modeling of C/N kinetic stability descriptors for PGM-free electrocatalysts at finite temperatures

The durability of platinum group metal-free (PGM-free) electrocatalysts is a major barrier to their usage in polymer electrolyte fuel cell cathodes. C and N removal from active sites may play an important role in the catalyst’s ability to maintain high activity. While C degradation mechanisms are kinetically controlled, previous studies have focused on thermodynamic descriptors. In this work, we develop a temperature-dependent kinetic descriptor of C and N stability using an electron beam-damage model. Our approach considers the electron beam energy threshold (EBET) describing the knock-on displacement of C and N atoms as a stability descriptor for atomic structures. The stability of different sites is calculated to be different showing this approach can discriminate between similar sites with varied configurations. Additionally, we provide important insight regarding TEM beam damage of proposed active sites. We calculate 60 keV electrons can damage some proposed active site structures even at room temperature.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Unraveling Hydrogen Induced Geochemical Reaction Mechanisms through Coupled Geochemical Modeling and Machine Learning

Underground hydrogen storage (UHS) provides a promising large-scale, long-term energy storage solution. A reasonable recovery of stored hydrogen is critical for a successful storage scheme. However, in subsurface reservoirs hydrogen is subject to active geochemical reactions that might result in hydrogen loss. In this study, we implemented a geochemical modeling approach coupled with an unsupervised machine learning technique called non-negative matrix factorization (NMF) to unravel the complex brine-rock-H 2 geochemical processes responsible for hydrogen losses, with particular focus on sulfate reduction reactions. NMF is applied to modeled mineral evolution and fluid component profiles to retrieve profiles that can be interpreted to more easily assess competing processes. NMF decouples simulated competing equilibrium reactions. This facilitates separation of overlapping reaction profiles from redox processes, dissolution fronts, and secondary precipitation while considering the effects of simulation parameters such as salinity, temperature, and total H 2 pressure. NMF successfully discriminates these competing effects in nonlinear ways, allowing robust interpretation. In addition, NMF reveals subtle coupled mineral associations and reaction fronts that are invisible to conventional model analysis. This integrated approach strengthens the conceptual understanding of complex nonlinear hydrogen-brine-rock interactions and advances geochemical research on UHS systems to resolve complexities in modeled geochemical systems without the need for direct experiments or prior knowledge. Furthermore, this study highlights the efficacy of combining geochemical modeling with machine learning techniques to enhance the interpretability of the intricate geochemical simulation output through deciphering the overlapping reaction path that cannot be achieved only using conventional analysis of geochemical models alone.

08 HYDROGEN↗

Refine your search to explore more results.