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Tianao Qin, Ruixin Chen, Rufu Qin and Yang Yu
Time series prediction is an effective tool for marine scientific research. The Hierarchical Temporal Memory (HTM) model has advantages over traditional recurrent neural network (RNN)-based models due to its online learning and prediction capabilities. G...
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Wenhao Sun, Yidong Zou, Yunhe Wang, Boyi Xiao, Haichuan Zhang and Zhihuai Xiao
In the practical production environment, the complexity and variability of hydroelectric units often result in a need for more fault data, leading to inadequate accuracy in fault identification for data-driven intelligent diagnostic models. To address th...
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Dibo Dong, Shangwei Wang, Qiaoying Guo, Yiting Ding, Xing Li and Zicheng You
Predicting wind speed over the ocean is difficult due to the unequal distribution of buoy stations and the occasional fluctuations in the wind field. This study proposes a dynamic graph embedding-based graph neural network?long short-term memory joint fr...
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Haojie Wang, Pingqing Fan, Xipei Ma and Yansong Wang
The intelligent identification of coal gangue on industrial conveyor belts is a crucial technology for the precise sorting of coal gangue. To address the issues in coal gangue detection algorithms, such as high false negative rates, complex network struc...
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Wen Tian, Xuefang Zhou, Jianan Yin, Yuchen Li and Yining Zhang
The complex layout of the airport surface, coupled with interrelated vehicle behaviors and densely mixed traffic flows, frequently leads to operational conflict risks. To address this issue, research was conducted on the recognition of characteristics an...
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