A Novel Web Tunnel Detection Method Based on Protocol Behaviors

Author(s):  
Fei Wang ◽  
Liusheng Huang ◽  
Zhili Chen ◽  
Haibo Miao ◽  
Wei Yang
Author(s):  
Jun Han ◽  
Guodong Chen ◽  
Tao Liu ◽  
Qian Yang

Due to the deformation of the tunnel and the abnormal outburst of internal facilities, the existing railway tunnel line needs to be inspected regularly. However, the existing detection methods have some shortcomings, such as large measurement interference, low efficiency, discontinuity of section, and independence with the track structure. Therefore, an automatic detection method of tunnel space clearance based on point cloud data is proposed. By fitting the central axis of the tunnel, the extraction can be realized at any position of the tunnel. The coordinate system of tunnel gauge detection based on rail top surface is established, and different types of tunnel gauge frames are introduced. The improved ray algorithm method is used to realize automatic detection and analysis of various tunnel types. Field experiments on existing railway tunnels show that the method can accurately obtain the limit point and size of the tunnel. The cross-section of transgression is obtained. It can meet the requirements of tunnel detection accuracy and has great practicability in tunnel disease detection.


Author(s):  
K. Pegg-Feige ◽  
F. W. Doane

Immunoelectron microscopy (IEM) applied to rapid virus diagnosis offers a more sensitive detection method than direct electron microscopy (DEM), and can also be used to serotype viruses. One of several IEM techniques is that introduced by Derrick in 1972, in which antiviral antibody is attached to the support film of an EM specimen grid. Originally developed for plant viruses, it has recently been applied to several animal viruses, especially rotaviruses. We have investigated the use of this solid phase IEM technique (SPIEM) in detecting and identifying enteroviruses (in the form of crude cell culture isolates), and have compared it with a modified “SPIEM-SPA” method in which grids are coated with protein A from Staphylococcus aureus prior to exposure to antiserum.


Author(s):  
Weihai Sun ◽  
Lemei Han

Machine fault detection has great practical significance. Compared with the detection method that requires external sensors, the detection of machine fault by sound signal does not need to destroy its structure. The current popular audio-based fault detection often needs a lot of learning data and complex learning process, and needs the support of known fault database. The fault detection method based on audio proposed in this paper only needs to ensure that the machine works normally in the first second. Through the correlation coefficient calculation, energy analysis, EMD and other methods to carry out time-frequency analysis of the subsequent collected sound signals, we can detect whether the machine has fault.


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