This, in turn, helps determine the nature and quality of the defect management process in software testing. Defect Rejection Ratio: (Number of defects rejected/Total number of defects detected) X 100 Defect Leakage Ratio: (Number of defects missed/Total number of defects detected) X 100 The ...
Moreover, given that the PQDs have large surface-to-volume ratio, these defects generated in core are inclined to transfer to surface sites and can be further passivated by surface ligands. This unusual characteristic not only reduces the trap densities of PQDs, but also further decreases the ...
Among them, there were 624 images of loose subgrade and 516 images of underground cavity, and these images were divided into training and testing datasets in a ratio of 1:4. To ensure smooth training, the file sizes of these images were kept below 200 KB, with a resolution not exceeding ...
all the evaluation indices are more or less improved relative to the pre-improvement period. However, there is still a substantial promotion space in detection accuracy, and the occurrence of misdetection and leakage phenomena still needs to be reduced. ...
Defect Leakage Rate: Defect Leakage Rate gives a measure of how well testers can detect defects based their missed bugs (a.k.a: bugs found by customer). It is calculated as a ratio of number defects found by customer and number of defects found by testers. ...
This, in turn, helps determine the nature and quality of the defect management process in software testing. Defect Rejection Ratio: (Number of defects rejected/Total number of defects detected) X 100 Defect Leakage Ratio: (Number of defects missed/Total number of defects detected)...
However, to overcome the sand leakage problem, the grid needs to become finer. Therefore, the pile cell size is encrypted and divided to 0.003 to meet the demand, and the finite element mesh model is shown in Figure 2 (take bulge imperfection–weak concrete defects as an example). Figure ...
In addition, the LT technique allows defect detection on large surfaces, and the excitation frequency can determine the depth of test defects with a good signal-to-noise ratio. Figure 5. Schematic of LT tests in the defect detection process. UVT It is well known that PT and LT are the...
In the proposed model, we introduce the weight matrix s, which represents the weight ratio of each channel. L1 and L2 refer to the two fully connected layers, while δ and σ denote the ReLU and sigmoid activation functions, respectively. In dynamic convolution, the attention weight s is def...
The NEU-DET dataset was divided into the training set and test set in an 8:2 ratio. 1440 pictures were fed to the network for training, and 360 pictures were used for testing the model. In terms of data augmentation, we attempted many tactics, including GridMask, Mixup, Mosaic, and Auto...