Browsing by Author "Liu, Yuxin"

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    Impact of recycler information sharing on supply chain performance of construction and demolition waste resource utilization
    (MDPI, 2022-03-24) Zheng, Haoxuan; Li, Xingwei; Zhu, Xiaowen; Huang, Yicheng; Liu, Zhili; Liu, Yuxin; Liu, Jiaxin; Li, Xiangye; Li, Yuejia; Li, Chunhui
    In recent years, the generation of a large amount of construction and demolition waste (CDW) has threatened the public environment and human health. The inefficient supply chain of CDW resource utilization hinders the green development of countries around the world, including China. This study aims to reveal the impact of information sharing regarding recyclers’ market demand forecast on the performance of CDW resource utilization supply chains. Therefore, this paper uses the incomplete information dynamic game method to establish and solve the decision-making model of the construction and demolition waste resource utilization supply chain under the conditions of recyclers sharing and not sharing their information. The paper then obtains the Bayesian equilibrium solution and the optimal expected profit for each party. Finally, a numerical simulation was used in order to verify the validity of the model and conclusions. The main conclusions are as follows. In the CDW resource utilization supply chain, if the recycler is more pessimistic about the market’s demand forecast, their information sharing makes the remanufacturer more motivated to improve their level of environmental responsibility. In addition, information sharing by recyclers is always beneficial in increasing the profit of the remanufacturer, but it also may make the recycler lose profit. When the efficiency of the environmental responsibility investment of remanufacturers is in a high range, information sharing increases the profits of recyclers. Conversely, information sharing has no significant effect on the profits of recyclers. The impact on the profits of the entire CDW resource utilization supply chain depends on the intensity of competition among channels, the market share of offline recycling channels and the efficiency of environmental responsibility investments.
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    Model-agnostic meta-learning for fault diagnosis of industrial robots
    (IEEE, 2023-10-16) Liu, Yuxin; Chen, Chong; Wang, Tao; Cheng, Lianglun; Qin, Jian
    The success of deep learning in the field of fault diagnosis depends on a large number of training data, but it is a challenge to achieve fault diagnosis of multi-axis industrial robots in the case of few-shot. To address this issue, this paper proposes a method called Model-Agnostic Meta-Learning (MAML) for fault diagnosis of industrial robots. Its goal is to train an effective industrial robot fault classifier using minimal training data. Additionally, it can learn to recognize faults in new scenarios with high accuracy based on the training data. Experimental results based on a six-axis industrial robot dataset show that the proposed method is superior to traditional convolutional neural network (CNN) and transfer learning, and that the diagnostic results with the same amount of data in few-shot cases are better than existing intelligent fault diagnosis methods.