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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.
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 strength, oxidation resistant refractory alloys are difficult to fabricate for commercial use in extreme environments.
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.
In manufacturing parts for industry using traditional molds and dies, about 70 percent to 80 percent of the time it takes to create a part is a result of a relatively slow cooling process.
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).