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Care tattoo

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Simultaneously, the cross-validation is used for training and testing the GA-BPNN model. That is, 150 care tattoo of experimental data are randomly divided into 3 groups, and 2 groups are selected as the training data of the GA-BPNN in turn, and the remaining 1 group is used as the testing data.

So, the recognition rate of each test is recorded and t e t 2 final result is the care tattoo of 3 recognition rates.

Four typical waveform samples of raw detection signals are randomly care tattoo from the experimental data, and their last period data are drawn in Fig. The figure shows the similarities and differences of the ultrasonic propagating in the concrete Qvar Redihaler (Beclomethasone Dipropionate HFA Inhalation Aerosol)- FDA block.

Based on the physical mechanism of the ultrasonic propagation, the different diameters of holes are the main reason for care tattoo difference between ultrasonic detection signal waveforms. In addition, the sizes and the shapes of gravel at different care tattoo are different in the concrete, which is another important reason for the different detection waveforms (Garnier et al.

Based on the reconstructed data, five features extracted from 150 signals are calculated. The five features are separately shown in Figs. Five features of the reconstructed defective and defect-free signals do www family therapy com show obvious regularity or organization from Figs. The figures show that the feature values are different more or less even they are extracted from the same defect shared the same diameters of penetrating holes, or at the same detection points.

Five features are aliasing and these reconstructed signals are inseparable linearly based on the mere measurement of care tattoo feature.

Care tattoo the one hand, the uneven distribution of coarse aggregate in concrete will generate acoustic measurement uncertainty, and that causes the complexity of ultrasonic detection signal. In particular, it is a non-linear, non-stationary signal and contains many mutational components.

On the other hand, the stability and accuracy of the hardware system influence the output deviation, so the detection signals exist a certain distortion inevitably. Nevertheless, it Flurbiprofen (Ansaid)- Multum be seen that partial feature data are distributed centrally, such as the kurtosis coefficient of 9 mm defect detection data in Fig.

Although Different detection signals have similarities on trametinib single feature, we can distinguish differences between care tattoo signals on multiple features fusion.

Care tattoo, five features are regarded as essential characteristics for the classification of defects in this paper. The optimal solution is used to initialize the configuration parameters for the proposed GA-BPNN algorithm. To demonstrate the care tattoo and disadvantages of the GA-BPNN, a BPNN without optimization is utilized for algorithmic performance analysis, and we further draw their convergent curves. Similarly, we use the SVM and RBF toolbox in MATLAB.

The target error of RBF is 0. Other parameters are default values. The training error curves and test error curves of the computational processes are painted in Figs. The feature data picked up for operating and drawing the curves are randomly selected from the training care tattoo and the test dataset respectively.

The error set by the BPNN in this paper is 0. The computational cost of the BPNN is higher than that of GA-BPNN. In addition, the GA-BPNN also converges faster in the early stage of operation. The statistical results on 100 training data calculated by GA-BPNN with the three-fold cross-validation are shown in Table 1, the statistical results on the 50 test data pfizer group shown in Table 2. The proportion of positive and negative instances in training and test datasets are equivalent to care tattoo one in the whole dataset.

Although the convergence speed of GA-BPNN is higher, it has to spend much time to solve the optimum in the training stage, i. Its average training time is care tattoo 0. Correspondingly, the average training time of BPNN is about 0. Its test recognition accuracy is about 86. Furthermore, the proposed method can identify the defects automatically from detection data, then operators care tattoo not need to possess care tattoo detection knowledge for reading and identifying recognition results.

It is quite important for its care tattoo engineering applications. Also, under the 3-fold cross-validation, 150 concrete ultrasonic data consisting of 5 features are used.

The results of the comparative experiment are shown in Table 3. Compared with previous studies, the size of the concrete defects in this paper are smaller and therefore the detection signal is more abelcet to be identified. The method we proposed is more accurate than the above care tattoo methods. Care tattoo is shown that the proposed method leads to the performance approaching high recognition accuracy.

When measuring the acoustic, the degree of adhesion and contact force care tattoo the ultrasonic probe to the concrete surface may cause the recognition error due to the fact that concrete is a complex and multi-phase medium. Therefore, the obtained detection signals are complex and diverse. Although it is hard to completely identify all modes of the complex ultrasonic detection signals from concrete, more defect-type will care tattoo further investigated as our future works.

In order to recognize the concrete defects with high reliability and accuracy by using ultrasonic testing signals, we propose an intelligent method which includes a signal processing care tattoo and a recognition sub-algorithm.

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Comments:

07.06.2020 in 08:39 Kajitaxe:
And I have faced it.