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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.
Often there are major challenges in developing diverse and complex human mobility metrics systematically and quickly.
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.
Water heaters and heating, ventilation, and air conditioning (HVAC) systems collectively consume about 58% of home energy use.
This work seeks to alter the interface condition through thermal history modification, deposition energy density, and interface surface preparation to prevent interface cracking.
Additive manufacturing (AM) enables the incremental buildup of monolithic components with a variety of materials, and material deposition locations.
Ceramic matrix composites are used in several industries, such as aerospace, for lightweight, high quality and high strength materials. But producing them is time consuming and often low quality.
This invention utilizes new techniques in machine learning to accelerate the training of ML-based communication receivers.
MAPSTER is a lightweight software package that automatically searches deployed laptops for geospatial data and complies metadata (GPS coordinates, file size, etc) at a central checkpoint.