Cameras see the world differently than humans. Resolution, equipment, lighting, distance and atmospheric conditions can impact how a person interprets objects on a photo.
Though Nell Barber wasn’t sure what her future held after graduating with a bachelor’s degree in psychology, she now uses her interest in human behavior to design systems that leverage machine learning algorithms to identify faces in a crowd.
In front of family and friends, Lt. Col. Jessica Critcher and Maj. Micah McCracken gave their final report on their eye-opening year as ORNL military fellows.
Practical fusion energy is not just a dream at ORNL. Experts in fusion and material science are working together to develop solutions that will make a fusion pilot plant — and ultimately carbon-free, abundant fusion electricity — possible.
ORNL scientists will present new technologies available for licensing during the annual Technology Innovation Showcase. The event is 9 a.m. to 3 p.m. Thursday, June 16, at the Manufacturing Demonstration Facility at ORNL’s Hardin Valley campus.
To achieve practical energy from fusion, extreme heat from the fusion system “blanket” component must be extracted safely and efficiently. ORNL fusion experts are exploring how tiny 3D-printed obstacles placed inside the narrow pipes of a custom-made cooling system could be a solution for removing heat from the blanket.
It’s a simple premise: To truly improve the health, safety, and security of human beings, you must first understand where those individuals are.
ORNL researchers used the nation’s fastest supercomputer to map the molecular vibrations of an important but little-studied uranium compound produced during the nuclear fuel cycle for results that could lead to a cleaner, safer world.
Researchers at Oak Ridge National Laboratory have empirically quantified the shifts in routine daytime activities, such as getting a morning coffee or takeaway dinner, following safer at home orders during the early days of the COVID-19 pandemic.
An Oak Ridge National Laboratory team developed a novel technique using sensors to monitor seismic and acoustic activity and machine learning to differentiate operational activities at facilities from “noise” in the recorded data.