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Precise Cutterhead Clogging Detection for Shield Tunneling Machine Based on Deep Residual Networks
Ruihong Wu,Chengjin Qin,Guoqiang Huang,Jianfeng Tao,Chengliang Liu 제어·로봇·시스템학회 2024 International Journal of Control, Automation, and Vol.22 No.3
During the construction process of tunnels, the cutterhead of shield tunneling machines may get cloggeddue to clay adhesion, which may seriously affect the efficiency of the project. Therefore, finding an intelligentdiagnosis method to detect the clogging status is of great importance. In this study, a deep residual network-basedmethod for diagnosing cutterhead clogging on shield tunneling machines is proposed. First, working state data ofthe shield tunneling machine is screened out, and parameters reflecting the clogging state are selected for furtheranalysis. After eliminating extreme outliers, an empirical formula is proposed to label the data. At the same time,several time-domain features of the selected excavation parameters within every five minutes are extracted. Thesefeatures are then fed into the proposed model as the input data to realize clogging detection. Because the originaldataset is imbalanced, the combination of f1-score and accuracy is used to evaluate the performance of the proposedmodel. The results show that the accuracy of the proposed algorithm reaches 95.71%, which is 1.21%, 2.84%,9.84%, 6.04%, and 0.86% higher than the support vector machine-based, random forest-based, AdaBoost-based,extreme gradient boosting-based and deep neural network-based methods. The f1 score of the proposed modelis 0.923, which is also 0.038, 0.042, 0.269, 0.169 and 0.02 higher than those compared methods. Therefore, theproposed deep residual network-based method can accurately detect cutterhead clogging conditions.
Ruihong Sun,Hongyuan Liu,Xiang Wang 대한영상의학회 2020 Korean Journal of Radiology Vol.21 No.5
The coronavirus disease 2019 (COVID-19) pneumonia is a recent outbreak in mainland China and has rapidly spread to multiple countries worldwide. Pulmonary parenchymal opacities are often observed during chest radiography. Currently, few cases have reported the complications of severe COVID-19 pneumonia. We report a case where serial follow-up chest computed tomography revealed progression of pulmonary lesions into confluent bilateral consolidation with lower lung predominance, thereby confirming COVID-19 pneumonia. Furthermore, complications such as mediastinal emphysema, giant bulla, and pneumothorax were also observed during the course of the disease.