1 - 10 of 13 Results
ORNL researchers have developed a deep learning-based approach to rapidly perform high-quality reconstructions from sparse X-ray computed tomography measurements.
In nuclear and industrial facilities, fine particles, including radioactive residues—can accumulate on the interior surfaces of ventilation ducts and equipment, posing serious safety and operational risks.
We presented a novel apparatus and method for laser beam position detection and pointing stabilization using analog position-sensitive diodes (PSDs).
The invention presented here addresses key challenges associated with counterfeit refrigerants by ensuring safety, maintaining system performance, supporting environmental compliance, and mitigating health and legal risks.
We have been working to adapt background oriented schlieren (BOS) imaging to directly visualize building leakage, which is fast and easy.
High and ultra-high vacuum applications require seals that do not allow leaks. O-rings can break down over time, due to aging and exposure to radiation. Metallic seals can damage sealing surfaces, making replacement of the original seal very difficult.
This invention utilizes new techniques in machine learning to accelerate the training of ML-based communication receivers.
Knowing the state of charge of lithium-ion batteries, used to power applications from electric vehicles to medical diagnostic equipment, is critical for long-term battery operation.
Current technology for heating, ventilation, and air conditioning (HVAC) and other uses such as vending machines rely on refrigerants that have high global warming potential (GWP).
The technology describes an electron beam in a storage ring as a quantum computer.