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2020 – today
- 2023
[j8]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): 3246-3259 (2023)- [j7]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) - [j6]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: 102456 (2023) - 2021
- [j5]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) - [j4]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): 1845-1861 (2021) - 2020
- [j3]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: 106246 (2020) - [j2]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): 2751-2766 (2020)
2010 – 2019
- 2019
- [j1]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): 10627-10638 (2019)
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