Various structural defects deteriorate tunnel operation status and threaten public safety.Current tunnel inspection methods face problems of low efficiency,high equipment expense,and difficult data management.Combinin...Various structural defects deteriorate tunnel operation status and threaten public safety.Current tunnel inspection methods face problems of low efficiency,high equipment expense,and difficult data management.Combining the deep learning model and the 3D reconstruction method based on structure from motion(SfM),this paper proposes a novel SfM-Deep learning method for tunnel inspection.The high-quality 3D tunnel model is constructed by using images taken every 1 m along the longitudinal direction.The instance segmentation of leakage in longitudinal images is realized using the mask region-based convolutional neural network deep learning model.The SfM-Deep learning method projects the texture of the images after defect recognition to the 3D model and realizes the visualization of leakage defects.By projecting the model to the design cylindrical surface and expanding it,the tunnel leakage area is quantified.Through its practical application in a Shanghai metro shield tunnel,the reliability of the proposed method was verified.The novel SfM-Deep learning method can help engineers efficiently carry out intelligent tunnel detection.展开更多
The conduction mechanism of stress induced leakage current （SILC） through 2nm gate oxide is studied over a gate voltage range between 1.7V and stress voltage under constant voltage stress （CVS）. The simulation res...The conduction mechanism of stress induced leakage current （SILC） through 2nm gate oxide is studied over a gate voltage range between 1.7V and stress voltage under constant voltage stress （CVS）. The simulation results show that the SILC is formed by trap-assisted tunnelling （TAT） process which is dominated by oxide traps induced by high field stresses. Their energy levels obtained by this work are approximately 1.9eV from the oxide conduction band, and the traps are believed to be the oxygen-related donor-like defects induced by high field stresses. The dependence of the trap density on stress time and oxide electric field is also investigated.展开更多
基金supported by the Key Field Science and Technology Project of Yunnan Province(Grant No.202002AC080002)the National Natural-Science Foundation of China(Grant No.52078377).
文摘Various structural defects deteriorate tunnel operation status and threaten public safety.Current tunnel inspection methods face problems of low efficiency,high equipment expense,and difficult data management.Combining the deep learning model and the 3D reconstruction method based on structure from motion(SfM),this paper proposes a novel SfM-Deep learning method for tunnel inspection.The high-quality 3D tunnel model is constructed by using images taken every 1 m along the longitudinal direction.The instance segmentation of leakage in longitudinal images is realized using the mask region-based convolutional neural network deep learning model.The SfM-Deep learning method projects the texture of the images after defect recognition to the 3D model and realizes the visualization of leakage defects.By projecting the model to the design cylindrical surface and expanding it,the tunnel leakage area is quantified.Through its practical application in a Shanghai metro shield tunnel,the reliability of the proposed method was verified.The novel SfM-Deep learning method can help engineers efficiently carry out intelligent tunnel detection.
文摘The conduction mechanism of stress induced leakage current （SILC） through 2nm gate oxide is studied over a gate voltage range between 1.7V and stress voltage under constant voltage stress （CVS）. The simulation results show that the SILC is formed by trap-assisted tunnelling （TAT） process which is dominated by oxide traps induced by high field stresses. Their energy levels obtained by this work are approximately 1.9eV from the oxide conduction band, and the traps are believed to be the oxygen-related donor-like defects induced by high field stresses. The dependence of the trap density on stress time and oxide electric field is also investigated.
