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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.
How fast is a vehicle traveling? For different reasons, this basic question is of interest to other motorists, insurance companies, law enforcement, traffic planners, and security personnel. Solutions to this measurement problem suffer from a number of constraints.
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.
Measurements of grid voltage and current are essential for the optimal operation of the grid protection and control (P&C) systems.
Water heaters and heating, ventilation, and air conditioning (HVAC) systems collectively consume about 58% of home energy use.
This invention utilizes new techniques in machine learning to accelerate the training of ML-based communication receivers.
Multi-terminal DC (MTdc) systems based on high-voltage DC (HVDC) transmission technology is an upcoming concept. In such systems, either asymmetric monopole or bi-pole systems are generally employed. Such systems are not suitable for easy expansion.
Stability performance of interconnected power grids plays crucial roles on their secure operation to prevent cascading failure and blackout.