The spatio-temporal evolution of wall-bounded turbulence is characterized by high nonlinearity, multi-scale dynamics, and chaotic nature, making its accurate prediction a significant challenge for ...

Advances in artificial intelligence (AI) are now opening new possibilities for faster and more accurate flood mapping, ...

BEIJING -- A team of Chinese scientists has unveiled a high-resolution atlas detailing the global distribution of lunar surface chemistry, a significant advance that fills a critical data gap in the ...

CNN in deep learning is a special type of neural network that can understand images and visual information. It works just like human vision: first it detects edges, lines and then recognizes faces and ...

Abstract: Image Super-Resolution (SR) has emerged as a critical task in various domains, allowing low-resolution (LR) photographs to be improved into their high-resolution (HR) equivalents. This study ...

With the rapid development of machine learning, Deep Neural Network (DNN) exhibits superior performance in solving complex problems like computer vision and natural language processing compared with ...

In the field of medical image analysis, CNN is one of the most widely applied deep learning architectures. Through multiple layers of convolution, pooling, and fully connected operations, CNNs can ...

Abstract: By using hierarchical features, Convolutional Neural Networks (CNNs) which is a Deep Learning method have shown notable improvements in image restoration (IR) tasks. However, poor ...