Qingwei Nie
Orcid: 0000-0003-3274-543X
According to our database1,
Qingwei Nie
authored at least 14 papers
between 2021 and 2025.
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Bibliography
2025
A blockchain-based LLM-driven energy-efficient scheduling system towards distributed multi-agent manufacturing scenario of new energy vehicles within the circular economy.
Comput. Ind. Eng., 2025
2024
Probing an Easy-to-Deploy Multi-Agent Manufacturing System Based on Agent Computing Node: Architecture, Implementation, and Case Study.
J. Comput. Inf. Sci. Eng., April, 2024
An augmented reality-assisted interaction approach using deep reinforcement learning and cloud-edge orchestration for user-friendly robot teaching.
Robotics Comput. Integr. Manuf., February, 2024
Probing an LSTM-PPO-Based reinforcement learning algorithm to solve dynamic job shop scheduling problem.
Comput. Ind. Eng., 2024
Probing a point cloud based expeditious approach with deep learning for constructing digital twin models in shopfloor.
Adv. Eng. Informatics, 2024
2023
A mixed perception-based human-robot collaborative maintenance approach driven by augmented reality and online deep reinforcement learning.
Robotics Comput. Integr. Manuf., October, 2023
A multi-agent and cloud-edge orchestration framework of digital twin for distributed production control.
Robotics Comput. Integr. Manuf., August, 2023
A transfer learning CNN-LSTM network-based production progress prediction approach in IIoT-enabled manufacturing.
Int. J. Prod. Res., 2023
2022
An Augmented Reality-Assisted Prognostics and Health Management System Based on Deep Learning for IoT-Enabled Manufacturing.
Sensors, 2022
Probing an intelligent predictive maintenance approach with deep learning and augmented reality for machine tools in IoT-enabled manufacturing.
Robotics Comput. Integr. Manuf., 2022
A multi-agent and internet of things framework of digital twin for optimized manufacturing control.
Int. J. Comput. Integr. Manuf., 2022
2021
A flexible configuration method of distributed manufacturing resources in the context of social manufacturing.
Comput. Ind., 2021
IEEE Access, 2021
A Novel Predictive Maintenance Method Based on Deep Adversarial Learning in the Intelligent Manufacturing System.
IEEE Access, 2021