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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.
Understanding building height is imperative to the overall study of energy efficiency, population distribution, urban morphologies, emergency response, among others. Currently, existing approaches for modelling building height at scale are hindered by two pervasive issues.
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.