Mr. Zhengfei Xin Profile

Zhengfei Xin

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Publications (3)

Proceedings Article | 3 January 2025 Paper

Research on fault prediction optimization of CNC machine tools based on ensemble learning

Li Yao, Yuwen Wan, Srdjan Damjanovic, Zhengfei Xin

Proceedings Volume 13442, 1344220 (2025) https://doi.org/10.1117/12.3052979

KEYWORDS: Machine learning, Performance modeling, Data modeling, Education and training, Air temperature, Random forests, Decision trees, Data processing, Support vector machines, Reliability

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Proceedings Article | 20 February 2024 Paper

Research on optimization of inventory demand forecast in cloud infrastructure supply chain

Li Yao, Zhengfei Xin, Yuwen Wan

Proceedings Volume 13064, 1306414 (2024) https://doi.org/10.1117/12.3015985

KEYWORDS: Data modeling, Clouds, Education and training, Machine learning, Performance modeling, Mathematical optimization, Decision trees, Statistical modeling, Genetic algorithms

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With the development of computer and cloud computing related technologies, cloud products play a more important role in the economic and cultural activities of enterprises, organizations and individuals. In recent years, numerous cloud service providers have emerged globally to offer cloud products to consumers. Cloud infrastructure is the physical computers that cloud service providers rely on in order to provide cloud products, and cloud infrastructure provides resources such as CPU, GPU, memory, hard disk, and network for cloud products. It can be said that cloud infrastructure is a particularly important aspect of cloud computing. When cloud service providers manage the supply chain of cloud infrastructure, they often have too much or too little inventory, which results in a waste of resources or a failure to meet user demand. Therefore, how to accurately predict the inventory demand of cloud infrastructure supply chain has become a problem that cloud service providers need to solve. In this paper, we propose a GA-LightGBM model for predicting the inventory demand of cloud infrastructure supply chain by comparing multiple models. In this paper, GA-LightGBM is experimentally verified and analyzed with the control model, and it is found that the average RMSE of GA-LightGBM is 358.8792, which is significantly higher than that of each model in the control group; in the process of multiple training and validation, the range of the RMSE and the standard deviation of the RMSE of GA-LightGBM are significantly smaller than that of each model in the control group. It can be seen that the GA-LightGBM model has higher prediction accuracy and stability than the models in the control group. It is recommended that cloud service providers adopt this model to optimize the management of cloud infrastructure supply chain inventory demand forecasting, so as to improve enterprise benefits. The GA-LightGBM model proposed in this paper will also complement research in related areas.

Proceedings Article | 16 February 2023 Paper

Research on the selection of logistics mode for clothing e-commerce returns and exchanges based on entropy weight TOPSIS method

Li Yao, Ximan Wang, Mengxiang Xu, Zhengfei Xin, Xiaofan Guan

Proceedings Volume 12591, 125912W (2023) https://doi.org/10.1117/12.2668588

KEYWORDS: Reverse modeling, Transportation, Pollution control, Matrices, Data modeling, Industry, Carbon, Fuzzy logic

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