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- W4385486576 abstract "The automation and intelligence of tunnel boring machine (TBM) are of great significance to ensure the construction personnel safe and the construction proceeding normally. Establishing an accurate operating state recognition model is an important part of TBM automation and intelligence. For this issue, the method of TBM time series segmentation, time series feature extraction and selection, and establishment of status recognition classifier is adopted. Firstly, the key attributes of the whole time series of TBM are segmented by the time series segmentation algorithm, so that each segment can represent an independent status. Then feature extraction and selection methods are used to extract features from time series segments, and feature selection is carried out based on the correlation between features and classification objectives. Finally, the decision tree model is established with the selected feature and corresponding objectives. The experimental results show that the accuracy, precision, and recall of the TBM status classifier established by this method is more than 95%, which can accurately identify the operating status of the TBM." @default.
- W4385486576 created "2023-08-03" @default.
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- W4385486576 date "2023-01-01" @default.
- W4385486576 modified "2023-09-23" @default.
- W4385486576 title "Recognition of Tunneling Boring Machine Operating Status Based on the Time Series Analysis" @default.
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- W4385486576 doi "https://doi.org/10.1007/978-981-99-4334-0_7" @default.
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