Basic purposes of standardization in thermophysical parameter measurement

1975 ◽  
Vol 18 (9) ◽  
pp. 1336-1338
Author(s):  
B. N. Oleinik ◽  
O. A. Sergeev ◽  
V. D. Mikina ◽  
Yu. I. Aleksandrov
Keyword(s):  
Thermophysical Parameter ◽  
Parameter Measurement

Aerodynamic parameter measurement using the wind driven manipulator - Inverse force measurement on wings

1997 ◽  
Author(s):  
Richard Ames ◽  
Richard Ames ◽  
N. Komerath ◽  
J. Magill ◽  
N. Komerath ◽  
...  
Keyword(s):  
Force Measurement ◽  
Parameter Measurement ◽  
Aerodynamic Parameter ◽  
Inverse Force

An Improved Airborne Single Observer Passive Location

2012 ◽  
Vol 157-158 ◽  
pp. 1533-1536
Author(s):  
Yong Wang ◽  
Chang Qiang Huang ◽  
Zheng Wang ◽  
Wang Xi Li
Keyword(s):  
Angular Velocity ◽  
Phase Difference ◽  
Measurement Problem ◽  
Doppler Frequency ◽  
Original Method ◽  
Parameter Measurement ◽  
Frequency Change ◽  
Passive Location ◽  
Change Rate ◽  
Domain Information

Using phase difference change rate’s augmentation to angular velocity, an improved passive location is developed,which solves the high precision parameter measurement problem of angular velocity in passive location and tracking via spatial-frequency domain information. The simulation shows that this method can reduce the difficulties of parameter measurement. The ranging error is mainly affected by the measurement error of phase difference change rate and doppler frequency change rate. Compared with the original method, it has higher passive location precision.

scholarly journals A Two-Step Phenotypic Parameter Measurement Strategy for Overlapped Grapes under Different Light Conditions

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4532
Author(s):  
Yubin Miao ◽  
Leilei Huang ◽  
Shu Zhang
Keyword(s):  
Edge Detection ◽  
Parameter Measurement ◽  
Average Error ◽  
Projection Area ◽  
Shape Fitting ◽  
Multi Scale ◽  
Image Pyramids ◽  
Phenotypic Parameter ◽  
Contour Fitting ◽  
Image Edge

Phenotypic characteristics of fruit particles, such as projection area, can reflect the growth status and physiological changes of grapes. However, complex backgrounds and overlaps always constrain accurate grape border recognition and detection of fruit particles. Therefore, this paper proposes a two-step phenotypic parameter measurement to calculate areas of overlapped grape particles. These two steps contain particle edge detection and contour fitting. For particle edge detection, an improved HED network is introduced. It makes full use of outputs of each convolutional layer, introduces Dice coefficients to original weighted cross-entropy loss function, and applies image pyramids to achieve multi-scale image edge detection. For contour fitting, an iterative least squares ellipse fitting and region growth algorithm is proposed to calculate the area of grapes. Experiments showed that in the edge detection step, compared with current prevalent methods including Canny, HED, and DeepEdge, the improved HED was able to extract the edges of detected fruit particles more clearly, accurately, and efficiently. It could also detect overlapping grape contours more completely. In the shape-fitting step, our method achieved an average error of 1.5% in grape area estimation. Therefore, this study provides convenient means and measures for extraction of grape phenotype characteristics and the grape growth law.

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