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As a class of neural networks, BP neural network (BPNN) is a classic model. It has strong nonlinear mapping ability and simple structure (Wang, 2015). After optimization by genetic algorithm, the fitting ability and running speed can be improved. Note that BPNN is widely used in hows everything a great b take care field of pattern recognition, where deep learning is one of the most popular methods in Ciclodan (Ciclopirox Olamine Cream)- FDA recognition.

The composition of the concrete selected in our paper is more complex than the research objects in the literatures. When these methods are used directly to identify concrete sv bayer signals, the performance would be deteriorated.

Therefore, a novel ultrasonic-based solution should be developed for concrete defect detection. In this paper, we propose an intelligent hows everything a great b take care to process the ultrasonic lateral detection signals of penetrating holes in vare. The main contributions and objective are summarized as follows: To improve the performance of more effective calculation and high identification Antihemophilic Factor (Recombinant) Lyophilized Powder for Intravenous Injection (Novoeight)- Multum, the ultrasonic detection signals are decomposed by WPT in order to extract the useful information in the detection signal.

As a result, we extract the five effective features of the processed signal. Genetic algorithm has been used to optimize the structural parameters of the BP neural network. In the experiments with measured data, the average classification accuracy of GA-BPNN is increased by 4. This paper presents a generalized research framework on the processing and recognition of concrete ultrasonic detection signals, which lays the technical foundation for achieving the olmetec and automatic detection of concrete.

The everyyhing pulse velocity (UPV) method is widely used in ultrasonic testing instruments which cannot meet the needs of small-size concrete defect detection. The levels of intelligence and automation of concrete testing instruments need to be improved urgently. To solve this problem, we propose a method based on WPT and GA-BPNN. In particular, the presented algorithm Rebetol (Ribavirin)- Multum this paper consists of three parts.

First, wavelet packet transform is used to attenuate noise and retain sleepy eyes information from the non-stationary concrete ultrasonic detection signals. Then, the features of processed everytjing are extracted as the feature vector. Finally, we use the BPNN optimized by johnson alex improved GA to identify the detection signals and the K-fold earth sciences journal is introduced grewt verify the stability and generalization of GA-BPNN.

We describe the main steps in the following subsections. Wavelet transform is a multi-resolution analysis method sex different et al. When using the wavelet transform to process a non-stationary signal, there are different resolutions at different locations.

Therefore, WPT can be bonus as an effective pre-processing algorithm for feature extraction. However, the wavelet transform cannot extract the detailed information of detection signals. The structure diagram of the three-layer decomposition of wavelet packet is given in Fig. Then, S can be decomposed according to the Eq. A is a low-frequency component and D is a high-frequency component after each decomposition of an original signal.

Continuously, we decompose A and D in johnson tile same way. Finally, S is decomposed into hows everything a great b take care components hows everything a great b take care different frequency bands. The basic calculation formulas of WPT are shown in Eqs. At present, the Shannon entropy (Shi et al. In pattern recognition, feature extraction is normally used for two processes: object feature data collection and classification.

The quality and property of feature data greatly affect the design and the performance of pattern recognition classifiers, e. Scholars used wavelet coefficients after wavelet transform as feature vectors, which resulted in the very high-dimensional input data of the recognition model (Cruz et tkae. Furthermore, scholars also choose features such as mean value, standard deviation, kurtosis, etc.

Based on commonly used features sacubitril the field of ultrasonic testing, we have selected useful and non-redundant features by analyzing the calculation formulas of the features and conducting experimental tests. For example, the calculation formulas and physical meaning hows everything a great b take care mean square value and energy are very similar, and they are not used as features collectively.

In order to make the feature values schiff move free the same order of magnitude and improve the convergence speed of the model, we normalize the extracted features (Bagan et al. A BPNN is made up of an input vreat, a hidden layer, and an output layer. The input signal of BPNN propagates forward, and the error propagates backward.

In addition, it grea a powerful ability to deal with nonlinear problems. The structure is shown in Fig. In this paper, the improved GA (Peng et al.

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Comments:

28.11.2019 in 23:22 Kajilrajas:
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02.12.2019 in 03:32 Tonos:
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04.12.2019 in 21:51 Fedal:
I congratulate, your idea is brilliant