Abstract: As PV systems become more integrated into power grids, challenges arise in optimizing power flow and maintaining stability amid fluctuating load conditions. Variations in solar energy output ...

This project investigates the application of Deep Neural Networks (DNNs) for automated fault classification and fault location in power transmission lines. Using data generated from a simulated 4-bus ...

Department of Environmental, Water and Agricultural Engineering, Faculty of Civil & Environmental Engineering, Technion−Israel Institute of Technology, Haifa 3200003, Israel Environmental Physics ...

Python has grown in popularity over the years to become one of the most popular programming languages for machine learning (ML) and artificial intelligence (AI) tasks. It has replaced many of the ...

We demonstrate that a neural network automatically solves, explains, and generates university-level problems from the largest Massachusetts Institute of Technology (MIT) mathematics courses at a human ...

We demonstrate that combining optical superresolution imaging with deep learning classification methods increases the speed and accuracy of assessing the biological affinities of fossil pollen taxa.

Deep learning neural networks are especially potent at dealing with structured data, such as images and volumes. Both modified LiviaNET and HyperDense-Net performed well at a prior competition ...

Wesfeiler-Lehman Neural Machine (WLNM) is a subgraph-based link prediction method leveraging deep learning to automatically learn graph structure features for link ...