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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.
Here we present a solution for practically demonstrating path-aware routing and visualizing a self-driving network.
We have been working to adapt background oriented schlieren (BOS) imaging to directly visualize building leakage, which is fast and easy.
Electrochemistry synthesis and characterization testing typically occurs manually at a research facility.
A novel molecular sorbent system for low energy CO2 regeneration is developed by employing CO2-responsive molecules and salt in aqueous media where a precipitating CO2--salt fractal network is formed, resulting in solid-phase formation and sedimentation.
Simurgh revolutionizes industrial CT imaging with AI, enhancing speed and accuracy in nondestructive testing for complex parts, reducing costs.