Machine learning (ML) is widely used for key tasks in Connected and Automated Vehicles
(CAV), including perception, planning, and control. However, its reliance on vehicular data …

Since the energy sector is the dominant contributor to global greenhouse gas emissions, the
decarbonization of energy systems is crucial for climate change mitigation. Two major …

Federated learning is an emerging learning paradigm where multiple clients collaboratively
train a machine learning model in a privacy-preserving manner. Personalized federated …

Advancements in renewable energy technology have significantly reduced the consumer
dependence on conventional energy sources for power generation. Solar energy has …

The accurate prediction of day-ahead Photovoltaic (PV) power can provide technical support
for complex solar management systems. This problem involves forecasting a long time …

Bearing in mind European Green Deal assumptions regarding a significant reduction of
green house emissions, electricity generation from Renewable Energy Sources (RES) is …

Accurate photovoltaic (PV) power forecasting is pivotal for optimizing the integration of RES
into the grid and guaranteeing proficient energy management. Concurrently, the sensitive …

In clinical practice, computed tomography (CT) is an important noninvasive inspection
technology to provide patients' anatomical information. However, its potential radiation risk is …

Photovoltaic power generation is currently the most mature technology and the largest scale
of application of solar energy utilization, which is of great significance in contributing to the …

Federated learning (FL) is an effective way to incorporate information provided by different
clients when a single local client is unable to provide sufficient training samples for …