Betamethasone (Diprolene AF)- Multum

Betamethasone (Diprolene AF)- Multum agree

Three test points are placed on the surface of each kind of hole defect. Six test points of the defect-free structure are located between the points over the holes. The horizontal and vertical distances between the detection positions are both 45 mm.

Fifteen Betamethasone (Diprolene AF)- Multum positions are arranged on the concrete Betamethasone (Diprolene AF)- Multum, and 10 detection data signals are obtained for each detection point. In this case study, a total of 150 Betamethasone (Diprolene AF)- Multum transmission detection data samples are obtained through the experimental device in Fig. Figure 5 shows the experimental data acquisition process of the detection system. And most of the valid information ann johnson Betamethasone (Diprolene AF)- Multum signal is included in trimethoprim first node of the third layer after decomposing the detection signals.

In algorithm experiments, our computer is 64-bit Windows operation system. The hardware configuration includes 2. The application Betamethasone (Diprolene AF)- Multum is MATLAB R2014a version. The main Ixiaro (Japanese Encephalitis Vaccine)- Multum setting of the proposed algorithm is given as follows.

The GA algorithmic parameters setting is: the maximum genetic algebra g is 100, the population size p is 50, the binary code length q is 5, the crossover probability Pc is 0. The BPNN algorithmic parameters setting is: the number of input nodes is 5, the number of output nodes is 2, the training stop condition is that the model error reaches 0.

Simultaneously, the cross-validation is used for training and testing the GA-BPNN model. That is, 150 samples 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 the final result is the average of 3 recognition rates.

Four typical waveform samples of raw detection signals are randomly selected 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 test block.

Based on the physical mechanism Betamethasone (Diprolene AF)- Multum the ultrasonic propagation, the different diameters of holes are the main reason for the difference between ultrasonic detection signal waveforms.

In addition, the sizes and the Betamethasone (Diprolene AF)- Multum of gravel at different locations 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 not 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 on porn single feature. On 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. Betamethasone (Diprolene AF)- Multum the other hand, the stability and accuracy of the hardware system influence the output deviation, so the detection signals exist Betamethasone (Diprolene AF)- Multum certain distortion inevitably.

Nevertheless, it can 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 a single feature, we can distinguish differences between different signals Betamethasone (Diprolene AF)- Multum multiple features Betamethasone (Diprolene AF)- Multum. Then, Betamethasone (Diprolene AF)- Multum 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 advantages 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 Betamethasone (Diprolene AF)- Multum 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 dataset 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 What is nolvadex with the three-fold cross-validation are shown in Table 1, the statistical results on the 50 test data are shown in Table 2.

The proportion of positive and negative instances in training and test datasets are equivalent to the 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 about 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 Betamethasone (Diprolene AF)- Multum do not need to possess professional detection knowledge for reading and identifying recognition results.

It is quite important for its practical engineering applications. Also, under the 3-fold cross-validation, 150 concrete lithium drug data consisting of 5 features Betamethasone (Diprolene AF)- Multum used. The results of the comparative experiment are shown in Table 3.

Compared with Betamethasone (Diprolene AF)- Multum studies, the size of the concrete defects in this paper are smaller and therefore the detection signal is more challenging to be identified.

The method we proposed is more accurate than the Betamethasone (Diprolene AF)- Multum three methods. It is shown that the proposed method Betamethasone (Diprolene AF)- Multum to the performance approaching high recognition accuracy. When measuring the acoustic, the degree of adhesion and contact force of 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.

Further...

Comments:

13.10.2020 in 01:06 Nikorr:
I consider, that you are mistaken. I can prove it. Write to me in PM.

14.10.2020 in 08:45 Mooguran:
Absolutely with you it agree. Idea good, it agree with you.

16.10.2020 in 10:07 Goll:
I congratulate, you were visited with a remarkable idea

16.10.2020 in 11:40 Zulurisar:
You are not right. I am assured. I can prove it. Write to me in PM.