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Weiguang Fang
ORCID
Publication Activity (10 Years)
Years Active: 2019-2023
Publications (10 Years): 8
Top Topics
Multidimensional Data
Manufacturing Process
Data Driven
Deep Learning
Top Venues
Int. J. Prod. Res.
J. Comput. Inf. Sci. Eng.
Robotics Comput. Integr. Manuf.
IEEE Internet Things J.
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Publications
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Daoyuan Liu
,
Yu Guo
,
Shaohua Huang
,
Weiguang Fang
,
Xu Tian
A stacking denoising auto-encoder with sample weight approach for order remaining completion time prediction in complex discrete manufacturing workshop.
Int. J. Prod. Res.
61 (10) (2023)
Weiwei Qian
,
Yu Guo
,
Hao Zhang
,
Shaohua Huang
,
Litong Zhang
,
Hailang Zhou
,
Weiguang Fang
,
Shanshan Zha
Digital twin driven production progress prediction for discrete manufacturing workshop.
Robotics Comput. Integr. Manuf.
80 (2023)
Weiguang Fang
,
Hao Zhang
,
Weiwei Qian
,
Yu Guo
,
Shaoxun Li
,
Zeqing Liu
,
Chenning Liu
,
Dongpao Hong
An Adaptive Job Shop Scheduling Mechanism for Disturbances by Running Reinforcement Learning in Digital Twin Environment.
J. Comput. Inf. Sci. Eng.
23 (5) (2023)
Shaohua Huang
,
Yu Guo
,
Nengjun Yang
,
Shanshan Zha
,
Daoyuan Liu
,
Weiguang Fang
A weighted fuzzy C-means clustering method with density peak for anomaly detection in IoT-enabled manufacturing process.
J. Intell. Manuf.
32 (7) (2021)
Weiwei Qian
,
Yu Guo
,
Kai Cui
,
Pengxing Wu
,
Weiguang Fang
,
Daoyuan Liu
Multidimensional Data Modeling and Model Validation for Digital Twin Workshop.
J. Comput. Inf. Sci. Eng.
21 (3) (2021)
Weiguang Fang
,
Yu Guo
,
Wenhe Liao
,
Karthik Ramani
,
Shaohua Huang
Big data driven jobs remaining time prediction in discrete manufacturing system: a deep learning-based approach.
Int. J. Prod. Res.
58 (9) (2020)
Weiguang Fang
,
Yu Guo
,
Wenhe Liao
,
Shaohua Huang
,
Nengjun Yang
,
Jinshan Liu
A Parallel Gated Recurrent Units (P-GRUs) network for the shifting lateness bottleneck prediction in make-to-order production system.
Comput. Ind. Eng.
140 (2020)
Shaohua Huang
,
Yu Guo
,
Daoyuan Liu
,
Shanshan Zha
,
Weiguang Fang
A Two-Stage Transfer Learning-Based Deep Learning Approach for Production Progress Prediction in IoT-Enabled Manufacturing.
IEEE Internet Things J.
6 (6) (2019)