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This fact sheet summarizes a plug load management system called ATLIS, or automatic type and location identification system. ATLIS was developed by the National Renewable Energy Laboratory and is being considered for commercial building applications.
Distribution systems are currently facing steep operational challenges as a result of the rapidly increasing integration of renewables and other distributed energy resources (DERs)at both the primary and secondary circuit levels. Distribution utilities and system operators have traditionally had some visibility of their primary circuits using low-frequency supervisory control and data acquisition systems, and they have had very poor if not zero visibility of the secondary circuits where the presence of DERs is constantly increasing. Therefore, this paper presents simulation studies to demonstrate the benefits of an advanced, high-fidelity sensor technology, called as the Meta-Alert System (MAS), developed by Electrical Grid Monitoring Ltd. (EGM), on the distribution grid. First, a reliable model of the EGM sensors is developed, and then two use cases - distribution system state estimation (DSSE) and fault identification - are simulated to evaluate the performance of the MAS technology. Simulation results on the Electric Power Research Institute J1feeder demonstrate that the MAS can effectively participate in system-level DSSE programs and can detect and locate faults faster than traditional distribution protection schemes.
This presentation will be presented at CAES codebreaker session on May 4th. The slides include two projects that demonstrate how virtual training can benefit from visualization technology. One is a collaboration project between the Applied Visualization Laboratory and College of Eastern Idaho for radiation safety and survey training. The other one is Idaho National Laboratory's hazards identification online training that utilized LiDAR technology.
Field-induced domain-wall dynamics in ferroelectric materials underpins multiple applications ranging from actuators to information technology devices and necessitates a quantitative description of the associated mechanisms including giant electromechanical couplings, controlled nonlinearities, or low coercive voltages. While the advances in dynamic piezoresponse force microscopy measurements over the last two decades have rendered visualization of polarization dynamics relatively straightforward, the associated insights into the local mechanisms have been elusive. This work explores the domain dynamics in model polycrystalline materials using a workflow combining deep-learning-based segmentation of the domain structures with nonlinear dimensionality reduction using multilayer rotationally invariant autoencoders (rVAE). The former allows unambiguous identification and classification of the ferroelectric and ferroelastic domain walls. Here, the rVAE discovers the latent representations of the domain wall geometries and their dynamics, consequently providing insight into the intrinsic mechanisms of polarization switching, that can further be compared to simple physical models. The rVAE disentangles the factors affecting the pinning efficiency of ferroelectric walls, offering insights into the correlation of ferroelastic wall distribution and ferroelectric wall pinning.
Elemental doping is widely used to improve the performance of cathode materials in lithium‐ion batteries. However, macroscopic/statistical investigation on how doping sites are distributed in the material lattice, despite being a key prerequisite for understanding and manipulating the doping effect, has not been effectively established. Herein, to solve this predicament, a universal strategy is proposed to quantitatively identify the locations of Al and Mg dopants in lithium‐rich layered oxides (LLOs). Solid evidence confirms that Al prefers to occupy the transition metal (TM) layer, while Mg evenly occupies both TM and Li layers. As a result, Mg significantly reduces the thickness of LiO 2 slabs at room temperature, which will increase the energy barrier of oxygen activation and enhance the structure stability of LLOs. The suppressed oxygen activity in Mg‐doped LLO can be kinetically unlocked at 55 °C. The different characteristics of Al and Mg enlighten an Al/Mg co‐doping strategy to optimize LLOs, which significantly improves the cycle performance while lifting the capacity. In conclusion, these insights from the quantitative identification of doping sites shed light on the manipulation of doping effects toward better cathodes.
Abstract Remarkable methane uptake is demonstrated experimentally in three metal‐organic frameworks (MOFs) identified by computational screening: UTSA‐76, UMCM‐152 and DUT‐23‐Cu. These MOFs outperform the benchmark sorbent, HKUST‐1, both volumetrically and gravimetrically, under a pressure swing of 80 to 5 bar at 298 K. Although high uptake at elevated pressure is critical for achieving this performance, a low density of high‐affinity sites (coordinatively unsaturated metal centers) also contributes to a more complete release of stored gas at low pressure. The identification of these MOFs facilitates the efficient storage of natural gas via adsorption and provides further evidence of the utility of computational screening in identifying overlooked sorbents.
Growing plants with modified cell wall compositions is a promising strategy to improve resistance to pathogens, increase biomass digestibility, and tune other important properties. In order to alter biomass architecture, a detailed knowledge of cell wall structure and biosynthesis is a prerequisite. We report here a glycan array-based assay for the high-throughput identification and characterization of plant cell wall biosynthetic glycosyltransferases (GTs). We demonstrate that different heterologously expressed galactosyl-, fucosyl-, and xylosyltransferases can transfer azido-functionalized sugar nucleotide donors to selected synthetic plant cell wall oligosaccharides on the array and that the transferred monosaccharides can be visualized “on chip” by a 1,3-dipolar cycloaddition reaction with an alkynyl-modified dye. The opportunity to simultaneously screen thousands of combinations of putative GTs, nucleotide sugar donors, and oligosaccharide acceptors will dramatically accelerate plant cell wall biosynthesis research.
Abstract Remarkable methane uptake is demonstrated experimentally in three metal‐organic frameworks (MOFs) identified by computational screening: UTSA‐76, UMCM‐152 and DUT‐23‐Cu. These MOFs outperform the benchmark sorbent, HKUST‐1, both volumetrically and gravimetrically, under a pressure swing of 80 to 5 bar at 298 K. Although high uptake at elevated pressure is critical for achieving this performance, a low density of high‐affinity sites (coordinatively unsaturated metal centers) also contributes to a more complete release of stored gas at low pressure. The identification of these MOFs facilitates the efficient storage of natural gas via adsorption and provides further evidence of the utility of computational screening in identifying overlooked sorbents.
Lignin is the largest source of bio-based aromatic compounds in nature, and its valorization is essential to the sustainability of lignocellulosic biorefining. Characterizing lignin-derived compounds remains challenging due to the heterogeneity of this biopolymer. Tandem mass spectrometry is a promising tool for lignin structural analytics, as fragmentation patterns of model compounds can be extrapolated to identify characteristic moieties in complex samples. This work extends previous resonance excitation type collision-induced dissociation (CID) methods that identified lignin oligomers containing β-Ο-4, β-5, and β-β bonds, to also identify characteristics of 5-5, β-1, and 4-Ο-5 dimers, enabled by quadrupole time-of-flight (QTOF) CID with energy-resolved mass spectrometry (ERMS). Overall, beam type ERMS offers in-depth structural information and could ultimately contribute to tools for high-throughput lignin dimer identification.
Abstract Membrane‐embedded transporters impart essential functions to cells as they mediate sensing and the uptake and extrusion of nutrients, waste products, and effector molecules. Promiscuous multidrug exporters are implicated in resistance to drugs and antibiotics and are highly relevant for microbial engineers who seek to enhance the tolerance of cell factory strains to hydrophobic bioproducts. Here, we report on the identification of small multidrug resistance (SMR) transporters in early‐branching anaerobic fungi (Neocallimastigomycetes). The SMR class of transporters is commonly found in bacteria but has not previously been reported in eukaryotes. In this study, we show that SMR transporters from anaerobic fungi can be produced heterologously in the model yeast Saccharomyces cerevisiae , demonstrating the potential of these proteins as targets for further characterization. The discovery of these novel anaerobic fungal SMR transporters offers a promising path forward to enhance bioproduction from engineered microbial strains.
A search for supersymmetry is presented in events with a single charged lepton, electron or muon, and multiple hadronic jets. The data correspond to an integrated luminosity of 138 fb –1 of proton-proton collisions at a center-of-mass energy of 13 TeV, recorded by the CMS experiment at the CERN LHC. The search targets gluino pair production, where the gluinos decay into final states with the lightest supersymmetric particle (LSP) and either a top quark-antiquark ($\text{t}\bar{\text{t}}$ pair, or a light-flavor quark-antiquark ($\text{q}\bar{\text{q}}$ pair and a virtual or on-shell W boson. The main backgrounds, $\text{t}\bar{\text{t}}$ pair and W+jets production, are suppressed by requirements on the azimuthal angle between the momenta of the lepton and of its reconstructed parent W boson candidate, and by top quark and W boson identification based on a machine-learning technique. The number of observed events is consistent with the expectations from standard model processes. Limits are evaluated on supersymmetric particle masses in the context of two simplified models of gluino pair production. Exclusions for gluino masses reach up to 2120 (2050) GeV at 95% confidence level for a model with gluino decay to a $\text{t}\bar{\text{t}}$ pair (a $\text{q}\bar{\text{q}}$ pair and a W boson) and the LSP. For the same models, limits on the mass of the LSP reach up to 1250 (1070) GeV.
The increasing prevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants with the ability to escape existing humoral protection conferred by previous infection and/or immunization necessitates the discovery of broadly reactive neutralizing antibodies (nAbs). Utilizing mRNA display, we identify a set of antibodies against SARS-CoV-2 spike (S) proteins and characterize the structures of nAbs that recognize epitopes in the S1 subunit of the S glycoprotein. These structural studies reveal distinct binding modes for several antibodies, including the targeting of rare cryptic epitopes in the receptor-binding domain (RBD) of S that interact with angiotensin-converting enzyme 2 (ACE2) to initiate infection, as well as the S1 subdomain 1. Further, we engineer a potent ACE2-blocking nAb to sustain binding to S RBD with the E484K and L452R substitutions found in multiple SARS-CoV-2 variants. We demonstrate that mRNA display is an approach for the rapid identification of nAbs that can be used in combination to combat emerging SARS-CoV-2 variants.
Highlights: • A method is presented to detect, locate, quantify, and visualize dense microcracks in HPFRCC. • Quantification of dense microcracks is realized using deep learning method for the first time. • The presented method uses deep learning models that are trained using a realistic dataset size. • The presented method has a high computation efficiency for identifying and quantifying cracks. • The presented method provides crack width with errors up to 50 μm and a R{sup 2} value of 0.984. High-performance fiber-reinforced cementitious composites (HPFRCCs) feature high mechanical strengths, crack resistance, and durability. Under excessive loading, HPFRCCs demonstrate dense microcracks that are difficult to identify using existing methods. This study presents a computer vision method for identification, quantification, and visualization of microcracks in HPFRCCs based on deep learning. The presented method integrates multiple deep learning models and computer vision techniques in a hierarchical architecture. The crack pattern (e.g., number, width, and spacing of cracks) are automatically determined from pictures without human intervention. This study shows that the presented method achieves an accuracy of 0.992 for crack detection and an accuracy finer than 50 μm (R{sup 2} > 0.984) for quantification of crack width when deep learning models are trained using only 200 pictures of HPFRCCs and 200 pictures of conventional concrete with incorporation of data augmentation. The presented method is expected to be also applicable to other materials featuring complex cracks.
Background: Artificial intelligence is increasingly utilized to aid in the interpretation of cardiac magnetic resonance (CMR) studies. One of the first steps is the identification of the imaging plane depicted, which can be achieved by both deep learning (DL) and classical machine learning (ML) techniques without user input. We aimed to compare the accuracy of ML and DL for CMR view classification and to identify potential pitfalls during training and testing of the algorithms. Methods: To train our DL and ML algorithms, we first established datasets by retrospectively selecting 200 CMR cases. The models were trained using two different cohorts (passively and actively curated) and applied data augmentation to enhance training. Once trained, the models were validated on an external dataset, consisting of 20 cases acquired at another center. We then compared accuracy metrics and applied class activation mapping (CAM) to visualize DL model performance. Results: The DL and ML models trained with the passively-curated CMR cohort were 99.1% and 99.3% accurate on the validation set, respectively. However, when tested on the CMR cases with complex anatomy, both models performed poorly. After training and testing our models again on all 200 cases (active cohort), validation on the external dataset resulted in 95% and 90% accuracy, respectively. The CAM analysis depicted heat maps that demonstrated the importance of carefully curating the datasets to be used for training. Conclusions: Both DL and ML models can accurately classify CMR images, but DL outperformed ML when classifying images with complex heart anatomy.
This study presents a deep learning based methodology for both remote sensing and design of acoustic scatterers. The ability to determine the shape of a scatterer, either in the context of material design or sensing, plays a critical role in many practical engineering problems. This class of inverse problems is extremely challenging due to their high-dimensional, nonlinear, and ill-posed nature. To overcome these technical hurdles, we introduce a geometric regularization approach for deep neural networks (DNN) based on non-uniform rational B-splines (NURBS) and capable of predicting complex 2D scatterer geometries in a parsimonious dimensional representation. Then, this geometric regularization is combined with physics-embedded learning and integrated within a robust convolutional autoencoder (CAE) architecture to accurately predict the shape of 2D scatterers in the context of identification and inverse design problems. Further, an extensive numerical study is presented in order to showcase the remarkable ability of this approach to handle complex scatterer geometries while generating physically-consistent acoustic fields. The study also assesses and contrasts the role played by the (weakly) embedded physics in the convergence of the DNN predictions to a physically consistent inverse design.
Here we propose a latent space dynamics identification method, namely tLaSDI, that embeds the first and second principles of thermodynamics. The latent variables are learned through an autoencoder as a nonlinear dimension reduction model. The latent dynamics are constructed by a neural network-based model that precisely preserves certain structures for the thermodynamic laws through the GENERIC formalism. An abstract error estimate is established, which provides a new loss formulation involving the Jacobian computation of autoencoder. The autoencoder and the latent dynamics are simultaneously trained to minimize the new loss. Computational examples demonstrate the effectiveness of tLaSDI, which exhibits robust generalization ability, even in extrapolation. In addition, an intriguing correlation is empirically observed between a quantity from tLaSDI in the latent space and the behaviors of the full-state solution.
In this work, the graph-theoretical concept of connected components is employed to extract the evolution of defect configurations in a polycrystalline aluminum structure containing ~8.3 million atoms. This graph-component approach is applied to reveal details of defect formation, transport, and transformation in the polycrystalline Al under large shear deformation. Building upon standard nearest neighbor analysis, graph theory and associated tools are used to reduce the multi-million-atom system into discrete component subgraphs that represent distinct structural defects. This method allows the automated identification, characterization, and tracking of defective regions within large volumes of data representing atomic-scale processes. Such analysis elucidates relationships between external stimuli, such as strain, and defect distributions, which have a large influence on material properties. The Graph Analytics for Large Atomistic Simulations (GALAS) codebase that implements this analysis, together with user guidance, is openly available at https://github.com/pnnl/galas.
Reducing the overall energy consumption and associated greenhouse gas emissions in the building sector is essential for meeting our future sustainability goals. Recently, smart energy metering facilities have been deployed to enable monitoring of energy consumption data with hourly or subhourly temporal resolution. This unprecedented data collection has created various opportunities for advanced data analytics involving load profiles (e.g., building energy benchmarking programs, building-to-grid integration, and calibration of urban-scale energy models). These applications often need preprocessing steps to detect daily load profile discords, such as: 1) outliers due to system malfunctions (the bad) and 2) irregular energy consumption patterns, such as those resulting from holidays (the ugly) compared to normal consumption patterns (the good). However, current preprocessing methods predominantly focus on filtering using statistical threshold values, which fail to capture the contextual discords of daily profiles. In addition, discord detection algorithms in building research are often aimed at finding individual building-level discords, which are not suitable at a large scale. Thus, here, we develop a method for automated load profile discord identification (ALDI) in a large portfolio of buildings (more than 100 buildings). Specifically, ALDI 1) uses the matrix profile (MP) method to quantify the similarities of daily subsequences in time series meter data, 2) compares daily MP values with typical-day MP distributions using the Kolmogorov-Smirnov test, and 3) identifies daily load profile discords in a large building portfolio. We evaluate ALDI using the metering data of both an academic campus and a residential neighborhood. Our results demonstrate that ALDI efficiently discovers measurement errors by system malfunctions and low energy consumption days in the academic campus portfolio, and it detects unique load shape patterns likely driven by occupant behavior and extreme weather conditions in the residential neighborhood.