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- Rafal Wojda
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- Venkatakrishnan Singanallur Vaidyanathan
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- Suman Debnath
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- Vladislav N Sedov
- Yacouba Diawara

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.

ORNL has developed a large area thermal neutron detector based on 6LiF/ZnS(Ag) scintillator coupled with wavelength shifting fibers. The detector uses resistive charge divider-based position encoding.

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.