The software tool developed by Stony Brook University uses self-supervised learning to detect long-term solar equipment damage weeks or years before manual inspections find it.
Objective To (1) develop and evaluate a machine learning model incorporating gait and physical activity to predict medial tibiofemoral cartilage worsening over 2 years in individuals without advanced ...
With such increased predictive knowledge of solar systems, these anomaly detectors can significantly reduce costs of O&M, a major component of project economics in solar development. There is great ...
Industrial automation is entering a new era with physical AI, where machine learning meets real-world motion control. AI-driven robotics and digital twins are closing the gap between simulation and ...
Researchers have recast diffusion in multicomponent alloys as a sum of individual contributions, called 'kinosons.' Using machine learning to compute the statistical distribution of the individual ...
Background Machine learning is an artificial intelligence technique, consisting of learning from data and making predictions (such as classifications), which could provide access to an injury risk ...
In food drying applications, machine learning has demonstrated strong capability in predicting drying rates, moisture ...
From SOCs to smart cameras, AI-driven systems are transforming security from a reactive to a predictive approach. This ...
Researchers in the Nanoscience Center at the University of Jyväskylä, Finland, have developed a pioneering computational ...
There is more than one way to describe a water molecule, especially when communicating with a machine learning (ML) model, says chemist Robert DiStasio. You can feed the algorithm the molecule's ...
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