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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.
Misalignment issues of the PWPT system have been addressed. The intercell power transformer has been introduced in order to improve load sharing of the system during a mismatch of the primary single-phase coil and the secondary multi-phase coils.
We have been working to adapt background oriented schlieren (BOS) imaging to directly visualize building leakage, which is fast and easy.
An ORNL invention proposes using 3D printing to make conductors with space-filling thin-wall cross sections. Space-filling thin-wall profiles will maximize the conductor volume while restricting the path for eddy currents induction.
The invention is related to the implementation of an bi-directional and isolated electric vehicle charger. The bidirectionality allows the electric vehicles to support the grid in case of disturbances thereby reducing the stress on the existing infrastructure.
This invention utilizes new techniques in machine learning to accelerate the training of ML-based communication receivers.
Additively manufacturing of the windings with a conductor distributed in the cross-section according to the Hilbert curve provides many benefits as it allows for the reduction of the high-frequency losses due to the reduction of the effective winding conductor size.