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Zhenzhong Xu, Bang Chen, Shenghan Zhou, Wenbing Chang, Xinpeng Ji, Chaofan Wei and Wenkui Hou
In the process of aircraft maintenance and support, a large amount of fault description text data is recorded. However, most of the existing fault diagnosis models are based on structured data, which means they are not suitable for unstructured data such...
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Shenghan Zhou, Chaofan Wei, Pan Li, Anying Liu, Wenbing Chang and Yiyong Xiao
Traditional aircraft maintenance support work is mainly based on structured data. Unstructured data, such as text data, have not been fully used, which means there is a waste of resources. These unstructured data contain a great storehouse of fault knowl...
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Linchao Yang, Guozhu Jia, Ke Zheng, Fajie Wei, Xing Pan, Wenbing Chang and Shenghan Zhou
At present, the research on fault analysis based on text data focuses on fault diagnosis and classification, but it rarely suggests how to use that information to troubleshoot faults reported in unmanned aerial vehicles (UAVs). Selecting the exact troubl...
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Wenbing Chang, Xinpeng Ji, Yinglai Liu, Yiyong Xiao, Bang Chen, Houxiang Liu and Shenghan Zhou
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Wenbing Chang, Yinglai Liu, Yiyong Xiao, Xingxing Xu, Shenghan Zhou, Xuefeng Lu and Yang Cheng
In this paper, cluster analysis and the XGBoost method are used to analyze the related symptoms of various types of young hypertensive patients, and finally guide patients to target treatment. Hypertension is a chronic disease that is common worldwide. T...
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Xinglong Yuan, Wenbing Chang, Shenghan Zhou and Yang Cheng
Sequential pattern mining (SPM) is an effective and important method for analyzing time series. This paper proposed a SPM algorithm to mine fault sequential patterns in text data. Because the structure of text data is poor and there are many different fo...
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