Abstract: In this monograph, the authors present an introduction to the framework of variational autoencoders (VAEs) that provides a principled method for jointly learning deep latent-variable models ...

Abstract: The virtual power plant (VPP) has been advocated as a promising way to aggregate massive distributed energy resources (DERs) in a distribution system (DS) for their participation in ...

Abstract: This paper presents a novel dual-loop event-triggered control framework designed to facilitate the formation control of unknown autonomous underwater vehicles (AUVs) operating under the ...

Abstract: Existing deep learning-based models can achieve a prompt diagnosis of operational anomalies by analyzing the audios emitted from power transformers. However, the practical abnormal data are ...

Abstract: Object detection is a critical component of autonomous driving perception. To achieve comprehensive environmental perception, mainstream methods commonly rely on multimodal sensor fusion.

Abstract: Predictive information is an important research direction in vehicle energy management. As the most intuitive item among numerous predictive information, the accurate and real-time ...

Abstract: The rapid advancements in artificial intelligence (AI), particularly the Large Language Models (LLMs), have profoundly affected our daily work and communication forms. However, it is still a ...

Abstract: Archetypal analysis (AA) is a matrix decomposition method that identifies distinct patterns using convex combinations of the data points denoted archetypes with each data point in turn ...

Abstract: Training self-supervised pretraining models for salient object detection (SOD) in RGB-D images is appealing, as it removes the costly demand of explicitly pixel-wise labels and exhibits ...

Abstract: Under foggy conditions, the atmospheric scattering effect diminishes the illumination intensity in images, causing a decline in the contrast of remote sensing images, which impacts the ...