scholarly journals Flow and spreading behaviors of coaxial jets wake

2020 ◽  
pp. 27-31
Author(s):  
Minh Duc Le, Ching Min Hsu Le
Keyword(s):  
Turbulence Intensity ◽  
Axial Velocity ◽  
Detection Method ◽  
Hot Wire ◽  
Visualization Technique ◽  
Recirculation Bubble ◽  
Swirling Jets ◽  
Smoke Flow ◽  
Bubble Length ◽  
Edge Detection Method

Flow and spreading behaviors of swirling jets using a dual-blockage disk are studied experimentally. The control and blockage disks are placed concentrically in tandem. The smoke flow patterns are obtained using the flow visualization technique. The axial velocity and turbulence intensity are detected using a 1-D hot-wire sensor. The jet spreading characteristics are illustrated by using an Edge Detection Method. Two pairs of lung-formed vortices and triangle-formed vortices are induced in downstream wake at Rec ≤ 200. Two vortices are found near the field at 200 < Rec < 700, while no toroidal structure was found above the reflected jet at Rec ≥ 700. The recirculation bubble length was increased with increasing Rec until Rec < 700. The axial velocity and turbulence intensity at 200 < Rec < 700 are significantly greater than those in other modes. At Rec ≥ 700, the shear-layer vortices are found far away from the control disk.

scholarly journals Cut-Edge Detection Method for Rice Harvesting Based on Machine Vision

Agronomy ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 590
Author(s):  
Zhenqian Zhang ◽  
Ruyue Cao ◽  
Cheng Peng ◽  
Renjie Liu ◽  
Yifan Sun ◽  
...  
Keyword(s):  
Machine Vision ◽  
Edge Detection ◽  
Detection Method ◽  
Region Of Interest ◽  
Average Error ◽  
Vertical Projection ◽  
Cut Edge ◽  
Fitting Method ◽  
Combine Harvester ◽  
Edge Detection Method

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.

Research on Improvement Algorithm of Image Edge Detection

2014 ◽  
Vol 539 ◽  
pp. 141-145
Author(s):  
Shui Li Zhang
Keyword(s):  
Cognitive Psychology ◽  
Edge Detection ◽  
High Frequency ◽  
Detection Method ◽  
Low Frequency ◽  
Frequency Information ◽  
Image Edge Detection ◽  
Improvement Algorithm ◽  
Image Edge ◽  
Edge Detection Method

This paper presents new theorems Stevens edge detection method based on cognitive psychology on. Firstly, based on the number of the image is decomposed into high-frequency and low-frequency information, and the high-frequency information extracted by subtracting the maximum number of images to the image after the filter, then the amount of high frequency information into psychological cognitive psychology based on Stevenss theorem. The algorithm suppression refined edge after the non-minimum, applications Pillar K-means algorithm to extract image edge. Experimental results show that: the brightness of the image is converted to the amount of psychological edge can better unify under different brightness values.

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