Weiguang Fang

According to our database, Weiguang Fang authored at least 8 papers between 2019 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

An Adaptive Job Shop Scheduling Mechanism for Disturbances by Running Reinforcement Learning in Digital Twin Environment.

Weiguang Fang, Hao Zhang, Weiwei Qian, Yu Guo, Shaoxun Li, Zeqing Liu, Chenning Liu, Dongpao Hong

J. Comput. Inf. Sci. Eng., October, 2023

A stacking denoising auto-encoder with sample weight approach for order remaining completion time prediction in complex discrete manufacturing workshop.

Daoyuan Liu, Yu Guo, Shaohua Huang, Weiguang Fang, Xu Tian

Int. J. Prod. Res., May, 2023

Digital twin driven production progress prediction for discrete manufacturing workshop.

Weiwei Qian, Yu Guo, Hao Zhang, Shaohua Huang, Litong Zhang, Hailang Zhou, Weiguang Fang, Shanshan Zha

Robotics Comput. Integr. Manuf., 2023

2021

A weighted fuzzy C-means clustering method with density peak for anomaly detection in IoT-enabled manufacturing process.

Shaohua Huang, Yu Guo, Nengjun Yang, Shanshan Zha, Daoyuan Liu, Weiguang Fang

J. Intell. Manuf., 2021

Multidimensional Data Modeling and Model Validation for Digital Twin Workshop.

Weiwei Qian, Yu Guo, Kai Cui, Pengxing Wu, Weiguang Fang, Daoyuan Liu

J. Comput. Inf. Sci. Eng., 2021

2020

Big data driven jobs remaining time prediction in discrete manufacturing system: a deep learning-based approach.

Weiguang Fang, Yu Guo, Wenhe Liao, Karthik Ramani, Shaohua Huang

Int. J. Prod. Res., 2020

A Parallel Gated Recurrent Units (P-GRUs) network for the shifting lateness bottleneck prediction in make-to-order production system.

Weiguang Fang, Yu Guo, Wenhe Liao, Shaohua Huang, Nengjun Yang, Jinshan Liu

Comput. Ind. Eng., 2020

2019

A Two-Stage Transfer Learning-Based Deep Learning Approach for Production Progress Prediction in IoT-Enabled Manufacturing.

Shaohua Huang, Yu Guo, Daoyuan Liu, Shanshan Zha, Weiguang Fang

IEEE Internet Things J., 2019