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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.
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BSUV-Net 2.0: Spatio-Temporal Data Augmentations for Video-Agnostic Supervised Background Subtraction
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DDU-Net: A Domain Decomposition-Based CNN for High-Resolution Image Segmentation on Multiple GPUs
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A Graph-Net with Node Embeddings to Detect False Data Injection Attacks in Photovoltaic Systems
Distributed energy resources (DER) contribute to the operational stability of the larger power grid both at utility-scale as well as commercial and residential scales in aggregated forms. These DER in-turn are susceptible to increasing cyber threats. An adversary can plug into the same local network that a field photovoltaic (PV) system uses to interconnect its data loggers and inverters and manipulate certain measurements collected from the network or trick existing irradiance and inverter readings through false data injection attacks (FDIA). Control routines that rely on these measurements can propagate the false data, impacting critical decisions that result in a suboptimal operation or even cause intentional harm leading to inverter-tripping or unscheduled loads that need to be shed. To detect FDIA in PV systems, the paper introduces an attention-based graph neural network with node embeddings and applied it to a simple prototypical DC-coupled microgrid with PV, energy storage, and load. The algorithm shows a detection accuracy of up to 98.95%. The proposed FDIA detection technique will provide micro-grid operators with an effective method to safeguard their systems, guaranteeing the secure and reliable operation.
Single-Net Continual Learning with Progressive Segmented Training
There is an increasing need of continual learning in dynamic systems, such as the self-driving vehicle, the surveillance drone, and the robotic system. Such a system requires learning from the data stream, training the model to preserve previous information and adapt to a new task, and generating a single-headed vector for future inference. Different from previous approaches with dynamic structures, this work focuses on a single network and model segmentation to prevent catastrophic forgetting. Leveraging the redundant capacity of a single network, model parameters for each task are separated into two groups: one important group which is frozen to preserve current knowledge, and secondary group to be saved (not pruned) for a future learning. A fixed-size memory containing a small amount of previously seen data is further adopted to assist the training. Without additional regularization, the simple yet effective approach of Progressive Segmented Training (PST) successfully incorporates multiple tasks and achieves the state-of-the-art accuracy in the single-head evaluation on CIFAR-10 and CIFAR-100 datasets. Moreover, the segmented training significantly improves computation efficiency in continual learning at the edge.
Maximizing the Cost-savings for Time-of-use and Net-metering Customers Using Behind-the-meter Energy Storage Systems.
Abstract not provided.
Recovering Power Factor Control Settings of Solar PV Inverters from Net Load Data.
Abstract not provided.
The QUANT-NET Testbed Development and Preliminary Results
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FeederGAN: Synthetic Feeder Generation via Deep Graph Adversarial Nets
This paper presents a novel, automated, generative adversarial networks (GAN) based synthetic feeder generation mechanism, abbreviated as FeederGAN. FeederGAN digests real feeder models represented by directed graphs via a deep learning framework powered by GAN and graph convolutional networks (GCN). Information of a distribution feeder circuit is extracted from its model input files so that the device connectivity is mapped onto the adjacency matrix and the device characteristics, such as circuit types (i.e., 3-phase, 2-phase, and 1-phase) and component attributes (e.g., length and current ratings), are mapped onto the attribute matrix. Then, Wasserstein distance is used to optimize the GAN and GCN is used to discriminate the generated graphs from the actual ones. A greedy method based on graph theory is developed to reconstruct the feeder using the generated adjacency and attribute matrices. Furthermore, our results show that the GAN generated feeders resemble the actual feeder in both topology and attributes verified by visual inspection and by empirical statistics obtained from actual distribution feeders.
Thermal optimality of net ecosystem exchange of carbon dioxide and underlying mechanisms
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Scaffold hopping by net photochemical carbon deletion of azaarenes
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Carbon Amendments Alter Microbial Community Structure and Net Mercury Methylation Potential in Sediments
ABSTRACT Neurotoxic methylmercury (MeHg) is produced by anaerobic Bacteria and Archaea possessing the genes hgcAB , but it is unknown how organic substrate and electron acceptor availability impacts the distribution and abundance of these organisms. We evaluated the impact of organic substrate amendments on mercury (Hg) methylation rates, microbial community structure, and the distribution of hgcAB + microbes with sediments. Sediment slurries were amended with short-chain fatty acids, alcohols, or a polysaccharide. Minimal increases in MeHg were observed following lactate, ethanol, and methanol amendments, while a significant decrease (∼70%) was observed with cellobiose incubations. Postincubation, microbial diversity was assessed via 16S rRNA amplicon sequencing. The presence of hgcAB + organisms was assessed with a broad-range degenerate PCR primer set for both genes, while the presence of microbes in each of the three dominant clades of methylators ( Deltaproteobacteria , Firmicutes , and methanogenic Archaea ) was measured with clade-specific degenerate hgcA quantitative PCR (qPCR) primer sets. The predominant microorganisms in unamended sediments consisted of Proteobacteria , Firmicutes , Bacteroidetes , and Actinobacteria . Clade-specific qPCR identified hgcA + Deltaproteobacteria and Archaea in all sites but failed to detect hgcA + Firmicutes . Cellobiose shifted the communities in all samples to ∼90% non- hgcAB -containing Firmicutes (mainly Bacillus spp. and Clostridium spp.). These results suggest that either expression of hgcAB is downregulated or, more likely given the lack of 16S rRNA gene presence after cellobiose incubation, Hg-methylating organisms are largely outcompeted by cellobiose degraders or degradation products of cellobiose. These results represent a step toward understanding and exploring simple methodologies for controlling MeHg production in the environment. IMPORTANCE Methylmercury (MeHg) is a neurotoxin produced by microorganisms that bioacummulates in the food web and poses a serious health risk to humans. Currently, the impact that organic substrate or electron acceptor availability has on the mercury (Hg)-methylating microorganisms is unclear. To study this, we set up microcosm experiments exposed to different organic substrates and electron acceptors and assayed for Hg methylation rates, for microbial community structure, and for distribution of Hg methylators. The sediment and groundwater was collected from East Fork Poplar Creek in Oak Ridge, TN. Amendment with cellobiose (a lignocellulosic degradation by-product) led to a drastic decrease in the Hg methylation rate compared to that in an unamended control, with an associated shift in the microbial community to mostly nonmethylating Firmicutes . This, along with previous Hg-methylating microorganism identification methods, will be important for identifying strategies to control MeHg production and inform future remediation strategies.
Impact of resonance decays on critical point signals in net-proton fluctuations
Not Available
Erratum to: Measurements of higher-order cumulants of multiplicity and net-electric charge distributions in inelastic proton-proton interactions by NA61/SHINE
This Erratum replaces, due to a discovery of coding mistakes, the following quantities: κ 3 /κ 1 of the h + − h − distribution presented in Fig. 6 and Table 6, κ 4 listed in Table 4, and $\hat{C}$ 4 presented in Fig. 7 and Table 5. All mentioned figures and tables were updated.