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: For three-dimensional (3D) imaging based on fringe projection profilometry (FPP), maximum fringe frequency selection and fringe frequencies allocation have a significant impact on the ...

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: Obstacle avoidance for autonomousaerial vehicles like quadrotors is a popular research topic. Most existing research focuses only on static environments, and obstacle avoidance in ...

Abstract: The incorporation of renewable energy sources (RESs) into power systems has significantly increased in recent years due to growing environmental, economic, and energy security concerns, ...

Abstract: Integration of distributed energy resources, such as photovoltaics, has expanded rapidly within power distribution networks in recent years. Existing management architectures face great ...

Abstract: Hyperspectral image denoising is crucial for accurate extraction of spectral information. However, current convolutional neural network (CNN)-based methods have inherent limitations, while ...

Abstract: Droop-controlled inverters reduce transient and steady-state frequency deviations (FDs) by providing frequency regulation (FR) power proportional to the FD during primary FR. However, with ...

Abstract: Breast cancer is one of the most prevalent diseases for women worldwide. Early and accurate ultrasound image segmentation plays a crucial role in reducing mortality. Although deep learning ...

Abstract: Reconstructing deformable anatomical structures from endoscopic videos is a pivotal and promising research topic that can enable advanced surgical applications and improve patient outcomes.

Abstract: Multivariate time series (MTS) anomaly detection is of great importance in both condition monitoring and malfunction identification within multi-sensor systems. Current MTS anomaly detection ...