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ORNL researchers have developed a deep learning-based approach to rapidly perform high-quality reconstructions from sparse X-ray computed tomography measurements.
How fast is a vehicle traveling? For different reasons, this basic question is of interest to other motorists, insurance companies, law enforcement, traffic planners, and security personnel. Solutions to this measurement problem suffer from a number of constraints.
The growing demand for electric vehicles (EVs) has necessitated significant advancements in EV charging technologies to ensure efficient and reliable operation.
The growing demand for renewable energy sources has propelled the development of advanced power conversion systems, particularly in applications involving fuel cells.
We have been working to adapt background oriented schlieren (BOS) imaging to directly visualize building leakage, which is fast and easy.
This invention presents a multiport converter (MPC) based power supply to charge the 12 V and 24 V auxiliary batteries in heavy duty (HD) fuel cell (FC) electric vehicle (EV) power train.
This invention presents an integrated strategy to reduce end-user electricity costs and grid carbon emissions by efficiently utilizing Distributed Energy Resources (DER) and grid-scale electrical energy storage systems, such as batteries.
No readily available public data exists for vehicle class and weight information that covers the entire U.S. highway network. The Travel Monitoring Analysis System, managed by the Federal Highway Administration covers only less than 1% of the US highway network.
This disclosure introduces an innovative tool that capitalizes on historical data concerning the carbon intensity of the grid, distinct to each electric zone.