Shanghua Mi
Zhejiang University
5 Papers
Shanghua Mi is an academic researcher from Zhejiang University. The author has contributed to research in topics: Computer science & Predictive maintenance. The author has an hindex of 3, co-authored 3 publications.
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Papers
Prediction maintenance integrated decision-making approach supported by digital twin-driven cooperative awareness and interconnection framework
TL;DR: The framework and integrated decision-making approach can guide the implementation of predictive maintenance with higher accuracy and reliability for industrial enterprises.
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Integrated Intelligent Green Scheduling of Predictive Maintenance for Complex Equipment based on Information Services
TL;DR: A structural framework of information sharing and service network is introduced to build a ubiquitous state data awareness environment for predictive maintenance service and an improved NSGA-II algorithm is utilized to solve this complicated two-objective optimization problem.
An Energy Efficiency Tool Path Optimization Method Using a Discrete Energy Consumption Path Model
TL;DR: In this article , the geometry features of the tool path are analyzed firstly, and the global energy consumption analysis, which includes a cutting energy analysis and driving energy analysis, is conducted.
Adaptive decoupling planning method for the product crowdsourcing design tasks based on knowledge reuse
Xiaoxie Gao,Yixiong Feng,Zhaoxi Hong,Shanghua Mi,Jianrong Tan +4 more
TL;DR: In this article , an adaptive intelligent decoupling planning method for relatively complex product crowdsourcing design tasks is proposed, simultaneously considering knowledge reuse and bilateral feature matching, and the resource competitiveness feedback strategy is taken to refine the decoupled planning scheme to meet resource competitive conditions.
4
A Framework of Joint Energy Provisioning and Manufacturing Scheduling in Smart Industrial Wireless Rechargeable Sensor Networks.
TL;DR: A novel double chains quantum genetic algorithm with Taboo search (DCQGA-TS) for J-EPMS to obtain a suboptimal solution to develop smart industrial wireless rechargeable sensor networks (SIWRSNs) in a smart factory environment.