Abstract: Deep learning has garnered extensive attention in hyperspectral image (HSI) processing. However, its application in HSI semantic segmentation tasks has been relatively limited. Although ...

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 adoption of voluntary environmental standards has emerged as a promising approach to coping with climate change and achieving sustainable development. While prior research has ...

Persistent Link: https://ieeexplore.ieee.org/servlet/opac?punumber=6221036 ...

Abstract: The traditional Rapidly-exploring Random Tree Star (RRT*) suffers from the low path generation efficiency, numerous invalid exploration points, and unsuitability for navigation in unknown ...

Abstract: This article simultaneously addresses the dual-rate and view constraints issues for the image-based visual servoing (IBVS) system of robot manipulators. Considering the low sampling ...

Abstract: Deep learning models have been widely investigated for computing and analyzing brain images across various downstream tasks such as disease diagnosis and age regression. Most existing models ...

Abstract: Hyperspectral imaging can capture abundant spectral information and reveal the spectral absorption properties of surface materials. Nevertheless, the trade-off in spatial resolution reduces ...

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: The iterative hard thresholding (IHT) algorithm is widely used for recovering sparse signals in compressed sensing. Despite the development of numerous variants of this effective algorithm, ...

Abstract: Accurate recognition and precise positioning of surface circular holes in aerospace components, which are crucial for automatic drilling and rivet leak detection, ensure the reliability of ...

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 ...