Shaohua Huang
Orcid: 0000-0003-4446-1733
According to our database1,
Shaohua Huang
authored at least 15 papers
between 2018 and 2025.
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Bibliography
2025
A two-channel collaborative filtering process template recommendation algorithm: RCAN - GGCNII - 2C.
Adv. Eng. Informatics, 2025
2024
An Online Quality Detection Method With Ensemble Learning on Imbalance Data for Wave Soldering.
J. Comput. Inf. Sci. Eng., February, 2024
Real-Time Quality Inspection Based on Transfer Learning and Feature Clustering for Wave Soldering.
IEEE Trans. Instrum. Meas., 2024
Generating the assembly instructions of helicopter subassemblies using the hierarchical pruning strategy and large language model.
J. Ind. Inf. Integr., 2024
A spatial-temporal feature fusion network for order remaining completion time prediction in discrete manufacturing workshop.
Int. J. Prod. Res., 2024
2023
Dynamic production bottleneck prediction using a data-driven method in discrete manufacturing system.
Adv. Eng. Informatics, October, 2023
A stacking denoising auto-encoder with sample weight approach for order remaining completion time prediction in complex discrete manufacturing workshop.
Int. J. Prod. Res., May, 2023
Digital twin driven production progress prediction for discrete manufacturing workshop.
Robotics Comput. Integr. Manuf., 2023
Profiling Cryptocurrency Influencers With Few-shot Learning via Contrastive Learning.
Proceedings of the Working Notes of the Conference and Labs of the Evaluation Forum (CLEF 2023), 2023
2021
A weighted fuzzy C-means clustering method with density peak for anomaly detection in IoT-enabled manufacturing process.
J. Intell. Manuf., 2021
2020
Big data driven jobs remaining time prediction in discrete manufacturing system: a deep learning-based approach.
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.
Comput. Ind. Eng., 2020
2019
A Two-Stage Transfer Learning-Based Deep Learning Approach for Production Progress Prediction in IoT-Enabled Manufacturing.
IEEE Internet Things J., 2019
An internet-of-things-based production logistics optimisation method for discrete manufacturing.
Int. J. Comput. Integr. Manuf., 2019
2018
Proceedings of the 11th IEEE International Conference on Cloud Computing, 2018