Large language models (LLMs) are rapidly being integrated into clinical workflows, supporting tasks such as diagnosis ...

Today's antipersonnel land mines are small and often have plastic casings that standard metal detectors cannot register.

An innovative partnership has yielded powerful new tools to help federal agencies rapidly synthesize complex data, historical ...

Abstract: Online non-intrusive load monitoring algorithms have captivated academia and industries as parsimonious solutions for household energy efficiency monitoring as well as a safety control, ...

The market for energy management technology is promising, driven by the increasing demand for energy-efficient solutions and the growing adoption of smart homes and buildings. Key trends and ...

Abstract: Non-intrusive load monitoring (NILM) breaks down household consumption to appliance-level, providing detailed insights into end-user electricity behavior using a single meter. NILM ...

In this repository are available codes for implementation of electrical loads classification and event detection in residential environments using PLAID dataset. But it can also be adapted to work ...

Machine learning is an Artificial Intelligence (or AI) application, an idea that came into being by giving machines access to data and letting them learn by themselves. AI has been making headlines, ...

This code repository implements four weight pruning algorithms designed to reduce the size of Zhang et al.'s sequence-to-point deep learning model for use in energy disaggreation / non-intrusive load ...

Non-intrusive load monitoring (NILM) is crucial because it allows for the efficient and easy collection of data on energy consumption in a given setting. NILM makes it possible to track energy ...