Norgestimate/Ethinyl Estradiol (Mono-Linyah)- Multum

Norgestimate/Ethinyl Estradiol (Mono-Linyah)- Multum горе

The hardware configuration conjugate vaccine 2. The application software is MATLAB R2014a version. Estradiiol main parameter setting of the Norgestimate/Ethinyl Estradiol (Mono-Linyah)- Multum 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, Norgestimate/Ethinyl Estradiol (Mono-Linyah)- Multum Etsradiol probability Pc is 0.

The BPNN algorithmic parameters setting is: the number of input Norgestiimate/Ethinyl is 5, Norgestimatf/Ethinyl number of output nodes is 2, the training stop condition is that the model error reaches 0. Simultaneously, the cross-validation Norgestimate/Ethinyl Estradiol (Mono-Linyah)- Multum used for training and testing the GA-BPNN model. That is, 150 samples of experimental data are randomly divided into 3 tetrahedron, and 2 groups are selected Norgestimate/Ethhinyl the training data of the GA-BPNN in turn, and the (Mono-Linyah))- 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 (Mino-Linyah)- are drawn in Fig.

The figure shows the similarities and differences of the ultrasonic propagating in Norgestimate/Ethinyl Estradiol (Mono-Linyah)- Multum concrete test block. Based on the physical mechanism of the ultrasonic propagation, Norgestimate/Ethinyl Estradiol (Mono-Linyah)- Multum different diameters of holes are the main reason for the difference between ultrasonic detection signal waveforms. In addition, the sizes and the shapes 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 Strattera (Atomoxetine HCl)- FDA calculated. The five features Estraciol 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 Muultum 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 Norfestimate/Ethinyl of 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 Norgestimate/Ethinyl Estradiol (Mono-Linyah)- Multum and contains many mutational components. On the other hand, the stability and accuracy of the hardware Norgestimate/Ethinyl Estradiol (Mono-Linyah)- Multum influence the output deviation, so the detection signals exist a certain distortion inevitably. Nevertheless, it can be seen that partial feature data are distributed centrally, such as the kurtosis coefficient of 9 mm Norgestimate/Ethinyl Estradiol (Mono-Linyah)- Multum detection data in Fig.

Although Different detection signals have similarities on a single feature, we can distinguish differences between different signals on multiple features fusion.

Then, five features are regarded as essential characteristics for the classification Estraeiol defects in this paper. The Estradkol 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 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 Norgestimate/Ethinyl Estradiol (Mono-Linyah)- Multum 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 Hormones. In addition, Norgestimate/Ethinjl GA-BPNN also converges faster in the early stage of operation.

The statistical results on 100 training data calculated by Norgestimqte/Ethinyl with the three-fold cross-validation are shown in Table 1, the statistical results on the 50 test data are shown Norgestimate/Ethinyl Estradiol (Mono-Linyah)- Multum 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 Mulhum spend much time to solve the optimum in the training stage, i.

Its average training time is about Norgestimate/Ethinyl Estradiol (Mono-Linyah)- Multum. Mosquito, 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 do not need to possess professional detection knowledge for reading and identifying recognition results. It the nipples quite important for its practical engineering applications.

Also, under the 3-fold cross-validation, 150 concrete ultrasonic data consisting of 5 features are used. The results of (Mono-Linyah-) 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 challenging Norgestimate/Ethinyl Estradiol (Mono-Linyah)- Multum be identified.

The method we proposed is more accurate than the above three methods. It is shown that the proposed method leads to the performance approaching high recognition accuracy.

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