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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.
We’ve developed a more cost-effective cable driven robot system for installing prefabricated panelized building envelopes. Traditional cable robots use eight cables, which require extra support structures, making setup complex and expensive.
We have been working to adapt background oriented schlieren (BOS) imaging to directly visualize building leakage, which is fast and easy.
The incorporation of low embodied carbon building materials in the enclosure is increasing the fuel load for fire, increasing the demand for fire/flame retardants.
The traditional window installation process involves many steps. These are becoming even more complex with newer construction requirements such as installation of windows over exterior continuous insulation walls.
Commercial closed-cell insulation foam boards reduce their thermal resistivity by up to 30% due to gas diffusion in and out of foam cells.
This invention utilizes new techniques in machine learning to accelerate the training of ML-based communication receivers.