X
Xinyu Li
Researcher at Huazhong University of Science and Technology
Publications - 262
Citations - 8875
Xinyu Li is an academic researcher from Huazhong University of Science and Technology. The author has contributed to research in topics: Job shop scheduling & Computer science. The author has an hindex of 34, co-authored 204 publications receiving 4662 citations.
Papers
More filters
Journal ArticleDOI
A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method
TL;DR: A new CNN based on LeNet-5 is proposed for fault diagnosis which can extract the features of the converted 2-D images and eliminate the effect of handcrafted features and has achieved significant improvements.
Journal ArticleDOI
A New Deep Transfer Learning Based on Sparse Auto-Encoder for Fault Diagnosis
Long Wen,Liang Gao,Xinyu Li +2 more
TL;DR: A new DTL method is proposed, which uses a three-layer sparse auto-encoder to extract the features of raw data, and applies the maximum mean discrepancy term to minimizing the discrepancy penalty between the features from training data and testing data.
Journal ArticleDOI
An effective hybrid genetic algorithm and tabu search for flexible job shop scheduling problem
TL;DR: An effective hybrid algorithm which hybridizes the genetic algorithm (GA) and tabu search (TS) has been proposed for the FJSP with the objective to minimize the makespan and the experimental results demonstrate that the proposed HA has achieved significant improvement for solving FJ SP regardless of the solution accuracy and the computational time.
Journal ArticleDOI
A transfer convolutional neural network for fault diagnosis based on ResNet-50
TL;DR: A new TCNN with the depth of 51 convolutional layers is proposed for fault diagnosis of ResNet-50 and achieves state-of-the-art results, which demonstrates that TCNN(ResNet- 50) outperforms other DL models and traditional methods.
Journal ArticleDOI
Digital twins-based smart manufacturing system design in Industry 4.0: A review
TL;DR: The definitions, frameworks, major design steps, new blueprint models, key enabling technologies, design cases, and research directions of digital twins-based SMS design are presented and it is expected that this survey will shed new light on urgent industrial concerns in developing new SMSs in the Industry 4.0 era.