A method for shock train leading edge detection based on differential pressure

Author(s):  
B Xiong ◽  
Z-G Wang ◽  
X-Q Fan ◽  
Y Wang

In order to make the shock train leading edge detection method more possible for operational application, a new detection method based on differential pressure signals is introduced in this paper. Firstly, three previous detection methods, including the pressure ratio method, the pressure increase method, and the standard deviation method, have been examined whether they are also applicable for shock train moving at different speeds. Accordingly, three experimental cases of back-pressure changing at different rates were conducted in this paper. The results show that the pressure ratio and the pressure increase method both have acceptable detection accuracy for shock train moving rapidly and slowly, and the standard deviation method is not applicable for rapid shock train movement due to its running time window. Considering the operational application, the differential pressure method is raised and tested in this paper. This detection method has sufficient temporal resolution for rapidly and slowly shock train moving, and can make a real-time detection. In the end, the improvements brought by the differential pressure method have been discussed.

Author(s):  
Daniel Le ◽  
Christopher Goyne ◽  
Roland Krauss ◽  
James McDaniel

2008 ◽  
Vol 24 (5) ◽  
pp. 1035-1041 ◽  
Author(s):  
D. B. Le ◽  
C. P. Goyne ◽  
R. H. Krauss

AIAA Journal ◽  
2020 ◽  
Vol 58 (9) ◽  
pp. 4068-4080 ◽  
Author(s):  
Chen Kong ◽  
Juntao Chang ◽  
Yunfei Li ◽  
Nan Li

2014 ◽  
Vol 9 (5) ◽  
pp. 1060
Author(s):  
Frank Zoko Ble ◽  
Matti Lehtonen ◽  
Ari Sihvola ◽  
Charles Kim

2013 ◽  
Vol 32 (8) ◽  
pp. 2296-2298 ◽  
Author(s):  
Fan ZHANG ◽  
Zhong-wei PENG ◽  
Shui-jin MENG

Agronomy ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 590
Author(s):  
Zhenqian Zhang ◽  
Ruyue Cao ◽  
Cheng Peng ◽  
Renjie Liu ◽  
Yifan Sun ◽  
...  

A cut-edge detection method based on machine vision was developed for obtaining the navigation path of a combine harvester. First, the Cr component in the YCbCr color model was selected as the grayscale feature factor. Then, by detecting the end of the crop row, judging the target demarcation and getting the feature points, the region of interest (ROI) was automatically gained. Subsequently, the vertical projection was applied to reduce the noise. All the points in the ROI were calculated, and a dividing point was found in each row. The hierarchical clustering method was used to extract the outliers. At last, the polynomial fitting method was used to acquire the straight or curved cut-edge. The results gained from the samples showed that the average error for locating the cut-edge was 2.84 cm. The method was capable of providing support for the automatic navigation of a combine harvester.


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