Researchers in Slovakia have demonstrated a machine-learning framework that predicts PV inverter output and detects anomalies using only electrical and temporal data, achieving 100% accuracy in ...

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.

When most of us think about artificial intelligence (AI), we envision algorithms, learning loops, and vast data models ...

TotalEnergies' deployment of machine learning at its Port Arthur, Texas, refinery demonstrates how predictive AI can ...

Discover Taiwo Feyijimi's work at the crossroads of AI, engineering, and learning. Explore his innovative frameworks shaping ...

Abstract: Power transformers are critical components in ensuring the continuous and stable operation of power systems. Failures in these units can lead to significant power outages and costly downtime ...

We have attempted to develop a Fracture Line Detection System using MATLAB and its Image Processing Toolbox. The system aims to assist radiologists by processing X-ray images to automatically ...

James Ratcliff joined Game Rant in 2022 as a Gaming News Writer. In 2023, James was offered a chance to become an occasional feature writer for different games and then a Senior Author in 2025. He is ...

Scientists in Japan have developed a new, highly efficient method for designing wireless power transfer (WPT) systems. Based on machine learning, the method enables a system to maintain stable voltage ...

Wireless power transfer (WPT) systems transmit electrical energy from a power source to a load without physical connectors or wires, using electromagnetic fields. This idea goes as far back as the ...

Abstract: Effective fault identification and diagnosis are critical in modern power systems to ensure operational reliability and reduce economic losses. This research describes a novel approach that ...