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At least 1,063 records · Page 59
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Multimodal Data Representation with Deep Learning for Extracting Cancer Characteristics from Clinical Text

This paper presents a multimodal data representation to improve the performance of deep learning models for extracting cancer key characteristics from unstructured text in pathology reports. Specifically, in addition to using the text as the input to deep learning models, we use concept unique identifiers (CUIs) as another source of information to the models. We analyze the performance of different text and CUI data representations, including word embeddings and bag of embeddings (BOE), with a convolutional neural network (CNN) and a fully connected multilayer perceptron neural network (MLP-NN). The high level document embeddings from text and CUI inputs are combined by concatenating them and then applying a classifier. The model is used for extracting cancer subsite and histology from pathology reports. These two classification tasks have a large number of labels, i.e. 317 for subsite and 556 for histology, with extreme class imbalance. We compare the performance of the developed DL models across the two tasks based on micro- and macro-F1 scores. The evaluation shows that a multi-channel DL model that utilizes text represented by word embeddings and CUIs represented by BOE outperforms other DL models. Also, this approach significantly improves the model performance on low prevalence classes.

Alawad, Mohammed↗
DOE OSTI2020

Graphene reinforced materials and related methods of manufacture

Graphene reinforced materials and related methods of manufacture are provided. The graphene reinforced materials include graphene sheet or scroll, graphene-polymer sheet or scroll, and graphene-carbon sheet or scroll, each having material properties that are attractive across a broad range of applications and industries. The graphene reinforced materials generally include monolayer or multilayer graphene that is synthesized by annealing a catalyst substrate within a CVD chamber, introducing a hydrocarbon gas as a carbon source with the CVD chamber to form a layer of graphene on the catalyst substrate, detaching the catalyst substrate from the layer of graphene, and rolling the layer of graphene onto itself to form a scroll, optionally with the addition of a polymer layer or carbonized layer on the graphene layer.

Vlassiouk, Ivan V.↗
DOE OSTI2020

Photonic near infrared heater

A multilayer photonic stack comprising a lower plurality of alternating layers comprising at least A and B and an upper plurality of alternating layers comprising at least C and D, layer A comprises at least one of Al, Au, W, Ag, Ni, Ti, Pt, and Cr, layer B comprises at least one of Al2O3, AlN, MgO, SiO2, TiO2, Si3N4, MgF2, Ta2O5, SiC, Si, Ge, and Indium Tin Oxide (ITO), and layers C and D comprise at least one of Al2O3, AlN, MgO, SiO2, TiO2, Si3N4, MgF2, Ta2O5, SiC, Si, Ge, and Indium Tin Oxide (ITO).

Prasher, Ravi Shankar↗
DOE OSTI2021

Subsurface Characterization and Machine Learning Predictions at Brady Hot Springs: Preprint

Subsurface data analysis, reservoir modeling, and machine learning (ML) techniques have been applied to the Brady Hot Springs (BHS) geothermal field in Nevada, USA to further characterize the subsurface and assist with optimizing reservoir management. Hundreds of reservoir simulations have been conducted in TETRAD-G and CMG STARS to explore different injection and production fluid flow rates and allocations and to develop a training data set for ML. This process included simulating the historical injection and production since 1979 and prediction of future performance through 2040. ML networks were created and trained using TensorFlow based on multilayer perceptron (MLP), long short-term memory (LSTM), and convolutional neural network (CNN) architectures. These networks took as input selected flow rates, injection temperatures, and historical field operation data and produced estimates of future production temperatures. This approach was first successfully tested on a simplified single fracture doublet system, followed by the application to the BHS reservoir. Using an initial BHS dataset with 37 simulated scenarios, the trained and validated network predicted the production temperature for 6 production wells with the mean absolute percentage error of less than 8%. In a complementary analysis effort, the principal component analysis applied to 13 BHS geological parameters revealed that vertical fracture permeability shows the strongest correlation with fault density and fault intersection density. A new BHS reservoir model was developed considering the fault intersection density as proxy for permeability. This new reservoir model helps to explore under-exploited zones in the reservoir. A data gathering plan to obtain additional subsurface data was developed; it includes temperature surveying for three idle injection wells, at which the reservoir simulations indicate high bottom-hole temperatures. The collected data assist with calibrating the reservoir model and may lead to converting these wells to producers to access under-exploited zones in the reservoir. Data gathering activities are planned for the first quarter of 2021.

40 EE - Geothermal Technologies Office (EE-4G)↗
DOE OSTI2020

Thermodynamic Consistent Neural Networks for Learning Material Interfacial Mechanics

For multilayer materials in thin substrate systems, interfacial failure is one of the most challenges. The traction-separation relations (TSR) quantitatively describe the mechanical behavior of a material interface undergoing openings, which is critical to understand and predict interfacial failures under complex loadings. However, existing theoretical models have limitations on enough complexity and flexibility to well learn the real-world TSR from experimental observations. A neural network can fit well along with the loading paths but often fails to obey the laws of physics, due to a lack of experimental data and understanding of the hidden physical mechanism. In this paper, we propose a thermodynamic consistent neural network (TCNN) approach to build a data-driven model of the TSR with sparse experimental data. The TCNN leverages recent advances in physics-informed neural networks (PINN) that encode prior physical information into the loss function and efficiently train the neural networks using automatic differentiation. We investigate three thermodynamic consistent principles, i.e., positive energy dissipation, steepest energy dissipation gradient, and energy conservative loading path. All of them are mathematically formulated and embedded into a neural network model with a novel defined loss function. A real-world experiment demonstrates the superior performance of TCNN, and we find that TCNN provides an accurate prediction of the whole TSR surface and significantly reduces the violated prediction against the laws of physics.

Zhang, Jiaxin↗
DOE OSTI2021

VERIFICATION OF TRISO FUEL BURNUP USING MACHINE LEARNING ALGORITHMS

Pebble Bed Reactors are fueled with fuel pebbles that are circulated multiple times through the reactor vessel before discharge. During the normal operation of a PBR, ejected pebbles are returned to the reactor or discharged depending on the fuel burnup and physical condition of the pebbles. The burnup measurement is usually based on detected radiation signatures of fission products accumulated in the pebble fuel over burnup. Previous research has shown that height of photopeaks of fission products, such as 134Cs, 137Cs, 154Eu, etc., can be used independently or in combination to infer or predict the level of burnup in the fuel. However, it remains challenging to measure such complex sources due to self-shielding effects, strong radiation background and intervening materials. Another operational challenge is the required high throughput of burnup measurement, which necessitates limited measurement time and thus impacts quality of measured gamma-ray spectra. Hence, advanced spectral analysis methods are needed to analyze the noisy gamma spectra and predict the burnup values. We propose to use machine learning (ML) method to interpret gamma-ray spectra and predict the burnup values of the pebbles. ML has achieved widespread success and adoption across a few domains that require pattern recognition and analysis in varied data types. In this work, we apply three proven ML approaches - multilayer perceptrons, convolutional neural networks, and transformers - to the task of predicting fuel burnup from measured gamma spectra, and compile a dataset of simulated spectra for training and validation of the ML models. In this paper, we will discuss the network architecture of these three ML approaches and compare the performance of the simplest of these (MLP) to a standard linear regression.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2021

Recent advances in nano-scale spatial resolution x-ray microscopy instrumentation at NSLS-II

X-ray microscopy is an invaluable and powerful characterization tool applied in many scientific fields, such as materials science, biology, environmental science, and energy research. In recent years it has been driven by rapid developments of novel technologies and systems resulting in imaging experiments elucidating structural inhomogeneities and chemical reactions at the nanometer scale. To obtain high spatial resolution comprehensive chemical and structural information, an X-ray microscope must be equipped with adequate capabilities and allow for simultaneous acquisition of multiple datasets. In recent years, a number of X-ray microscopes have been designed, constructed, and commissioned at NSLS-II. Here we provide an overview of the microscopy instrumentation development program at NSLS-II and specifically focus on the multilayer Laue lens-based hard X-ray nanoprobe optimized for ~10 nm spatial resolution imaging, its current status, and future upgrades along with recently constructed Kirkpatrick-Baez based scanning microscope designed for ~100 nm spatial resolution experiments.

36 MATERIALS SCIENCE↗
DOE OSTI2022

In-Line Membrane Thickness Mapping with Real-Time Data Processing

The goal of this project is commercialization of a novel, patented by NREL, non-contact, in-line quality control thickness mapping tool that will enable 100% area inspection of multilayer polymer film assemblies, such as a PEM on a casting substrate, during continuous high-volume R2R manufacturing.

ENGINEERING,HYDROGEN↗
DOE OSTI2022

Broadband and omnidirectional polymer antireflection coatings

A method for generating antireflective coatings for polymeric substrates using a deposition process and/or a dissolving process can provide a coating onto the outer surface of the substrate. Some embodiments can include a GLAD generated fluoropolymer coating or a co-evaporated fluoropolymer coating on a substrate that may achieve ultralow refractive index as well as improved adhesion and durability properties on polymeric substrates. In some embodiments, the deposition process is performed such that a fluoropolymer can be evaporated to form chain fragments of the fluoropolymer. The chain fragments diffused into the substrate can subsequently re-polymerize, interlocking with the polymer chains of the substrate. In some embodiments, the co-evaporation process can form a nanoporous polymer chain scaffold of the fluoropolymer, from which a sacrificial material can be dissolved out. The formed coating can be a multilayer or continuously-graded antireflective coating that has strong adhesion with the substrate.

Wang, Baomin↗
DOE OSTI2022

Functional high-performance fiber structure

A method is provided for growing a fiber structure, where the method includes: obtaining a substrate, growing an array of pedestal fibers on the substrate, growing fibers on the pedestal fibers, and depositing a coating surrounding each of the fibers. In another aspect, a method of fabricating a fiber structure includes obtaining a substrate and growing a plurality of fibers on the substrate according to 1½D printing. In another aspect, a multilayer functional fiber is provided produced by, for instance, the above-noted methods.

Pegna, Joseph↗
DOE OSTI2022

Evaluating HPC Kernels for Processing in Memory

Memory subsystems contribute significantly to the performance and energy efficiency of high-performance computing (HPC) applications. Traditional memory technologies with conventional organization (e.g., DRAM) are struggling to keep up with the increasing memory requirements of modern applications. Techniques such as multilayer cache hierarchy and out-of-order execution are still falling short of mitigating the penalty incurred by memory accesses. Processing-in-memory (PIM), which involves moving memory-intensive kernels to memory for execution instead of bringing the data to the processing unit, is emerging as a promising technique. PIM has recently received traction among computer architecture researchers, and the increasing research activity surrounding this technique indicates its potential to alleviate main memory performance bottlenecks. In this paper, we characterize and identify memory-intensive HPC kernels, perform a first-order evaluation of the PIM technique for selected HPC kernels, quantify performance deviation, and analyze the key factors that affect PIM efficiency.

Asifuzzaman, Kazi↗
DOE OSTI2022

Piezoelectric-triboelectric heel charger to generate electricity from locomotion using level mechanism and mechanical SSHI boosting circuit

The disclosure provides an electricity generating insert for a piece of footwear, the insert can be removably placed in the heel portion, e.g. under the insole. The insert comprises a multilayer piezoelectric stack that alternatively flexes under the compression-decompression that occurs during locomotion, which flexing causes friction in the stack to generate electricity capable of charging electronic devices and the like, e.g. via a port on the footwear.

Wang, Ya↗
DOE OSTI2023

WavPool: A New Block for Deep Neural Networks

Modern deep neural networks comprise many operational layers, such as dense or convolutional layers, which are often collected into blocks. In this work, we introduce a new, wavelet-transform-based network architecture that we call the multi-resolution perceptron: by adding a pooling layer, we create a new network block, the WavPool. The first step of the multi-resolution perceptron is transforming the data into its multi-resolution decomposition form by convolving the input data with filters of fixed coefficients but increasing size. Following image processing techniques, we are able to make scale and spatial information simultaneously accessible to the network without increasing the size of the data vector. WavPool outperforms a similar multilayer perceptron while using fewer parameters, and outperforms a comparable convolutional neural network by ~ 10% on relative accuracy on CIFAR-10.

McDermott, Samuel D.↗
DOE OSTI2023

First Results From Nb3Sn Coatings of 2.6 GHz Nb SRF Cavities Using DC Cylindrical Magnetron Sputtering System

A DC cylindrical magnetron sputtering system has been commissioned and operated to deposit Nb3Sn onto 2.6 GHz Nb SRF cavities. After optimizing the deposition conditions in a mock-up cavity, Nb-Sn films are deposited first on flat samples by multilayer sequential sputtering of Nb and Sn, and later annealed at 950°C for 3 hours. X-ray diffraction of the films showed multiple peaks for the Nb3Sn phase and Nb (substrate). No peaks from any Nb- Sn compound other than Nb3Sn were detected. Later three 2.6 GHz Nb SRF cavities are coated with ~1 μm thick Nb3Sn. The first Nb3Sn coated cavity reached close to Eacc = 8 MV/m, demonstrating a quality factor Q0 of $3.2 \times 108$ at Tbath = 4.4 K and Eacc = 5 MV/m, about a factor of three higher than that of Nb at this temperature. Q0 was close to $1.1 \times 109$, dominated by the residual resistance, at 2 K and Eacc = 5 MV/m. The Nb3Sn coated cavities demonstrated Tc in the range of $17.9 -- 18$ K. Here we present the commissioning experience, system optimization, and the first results from the Nb3Sn fabrication on flat samples and SRF cavities.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

First Results from Nb3Sn Coatings of 2.6 GHz Nb SRF Cavities Using DC Cylindrical Magnetron Sputtering System

A DC cylindrical magnetron sputtering system has been commissioned and operated to deposit Nb3Sn onto 2.6 GHz Nb SRF cavities. After optimizing the deposition conditions in a mock-up cavity, Nb-Sn films are deposited first on flat samples by multilayer sequential sputtering of Nb and Sn, and later annealed at 950 {\deg}C for 3 hours. X-ray diffraction of the films showed multiple peaks for the Nb3Sn phase and Nb (substrate). No peaks from any Nb3Sn compound other than Nb3Sn were detected. Later three 2.6 GHz Nb SRF cavities are coated with ~1 $\mu$m thick Nb3Sn. The first Nb3Sn coated cavity reached close to Eacc = 8 MV/m, demonstrating a quality factor Q0 of 3.2 x 108 at Tbath = 4.4 K and Eacc = 5 MV/m, about a factor of three higher than that of Nb at this temperature. Q0 was close to 1.1 x 109, dominated by the residual resistance, at 2 K and Eacc = 5 MV/m. The Nb3Sn coated cavities demonstrated Tc in the range of 17.9 - 18 K. Here we present the commissioning experience, system optimization, and the first results from the Nb3Sn fabrication on flat samples and SRF cavities.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Evaluation of Surface Crack Formation in Photovoltaic Backsheets Using Fragmentation and Finite Element Simulations: Preprint

Backsheet cracking is among the most commonly observed degradation modes of photovoltaic (PV) modules in the field. Cracks can reduce the ability of backsheets to fulfil their functions, for example, protection of the modules from the environment or electrical insulation. This work presents an evaluation of the degradation and cracking propensity of two backsheets during accelerated IEC TS 62788-7-2 A3 (International Electrotechnical Commission) aging with the fragmentation test: a co-extruded polyamide backsheet (AAA), and a laminated multilayer backsheet with a polyethylene terephthalate core and outer layer and ethylene vinyl acetate inner layer (PPE). Results show the surface embrittlement of the AAA outer layer during exposure. A longer time of exposure causes cracks to form at lower strains during stretching and creates deeper cracks. Accordingly, Young's modulus of the outer layer increases, as measured by cross-sectional nanoindentation. PPE exhibits cracking after exposure as well. While no similar increase of modulus or crack depth can be observed, the outer layer of PPE exhibits more obvious signs of erosion during exposure, including progressive morphological changes and thickness losses. A finite element model to simulate surface crack formation is devised, based on initially zero-thickness decohesion elements. Decohesion criteria define a critical stress, at which these elements grow, and cracks can begin to form. These criteria are obtained via parameter optimization by comparison between simulation and experiment. The model is used to interpret crack formation in both backsheets.

backsheet↗