Shaobo Li

TL;DR: A deep neural network based few-shot learning approach for rolling bearing fault diagnosis with limited data availability based on the siamese neural network, which learns by exploiting sample pairs of the same or different categories.

TL;DR: This paper proposed IDSCNN, a novel bearing fault diagnosis algorithm based on ensemble deep convolutional neural networks and an improved Dempster–Shafer theory based evidence fusion, which can achieve better fault diagnosis performance than existing machine learning methods.

TL;DR: In this paper, a survey of state-of-the-art deep learning methods for defect detection is presented, focusing on three aspects, namely method and experimental results, and the core ideas and codes of studies related to high precision, high positioning, rapid detection, small object, complex background, occluded object detection and object association.

TL;DR: A generative machine learning model (MatGAN) based on a generative adversarial network (GAN) for efficient generation of new hypothetical inorganic materials and is expected to be used to greatly expand the range of the design space for inverse design and large-scale computational screening of in organic materials.

TL;DR: A transfer learning algorithm based on Bi-directional Long Short-Term Memory (BLSTM) recurrent neural networks for RUL estimation is proposed, in which the models can be first trained on different but related datasets and then fine-tuned by the target dataset.