Deep transfer learning-based fault diagnosis of machines is achieved based on the
assumption that the source and target domain data could be centralized to assess the …

The success of deep transfer learning in fault diagnosis is attributed to the collection of high-
quality labeled data from the source domain. However, in engineering scenarios, achieving …

Deep convolution neural networks (CNN) have shown their efficacy in face recognition tasks
due to their ability to extract highly discriminant face representations from face images. On …

Urban meshes semantic segmentation is essential for comprehending the 3D real-world
environments, as it plays a vital role across various application domains, including digital …

The convolutional neural networks (CNNs) functioning on geometric learning for the urban
large-scale 3D meshes are indispensable because of their substantial, complex, and …

Superfacets are generated by clustering adjacent mesh faces that share similar
characteristics, which can serve as processing units in downstream mesh applications …

Metric Learning Assisted Aging Optical Transmitter Identification Based on the Constellation
Diagram Page 1 Metric Learning Assisted Aging Optical Transmitter Identification Based on the …

Recent convolutional neural networks (CNNs) in geometric deep-learning for 3D meshes
are inadequate in a natural generalisation of CNNs that reduces sample complexity by …