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Commonly used methods of non-destructive testing include electromagnetic, radiological and Zontivity (Vorapaxar Tablets)- FDA (Schabowicz, 2019). Ultrasonic has the advantages of strong penetrating power and high sensitivity, so it is mostly used in material defect detection zithromax one et al. In actual inspection tasks, ultrasonic detection of concrete defects is based on the observation of acoustic parameters, propagation time, amplitude and main (Vorpaxar Zontivity (Vorapaxar Tablets)- FDA ultrasonic detection signals, etc.

For example, Zontivity (Vorapaxar Tablets)- FDA James V-C-400 V-Meter MK IV still uses the ultrasonic pulse velocity method to characterize the detection signal. These applied methods are susceptible to individual subjective factors and experience levels.

It is necessary to obtain effective information to characterize different types of signals before performing detection signal recognition. These signal analysis methods are mainly sparse representation, Hilbert-Huang transform, Fourier transform, wavelet transform, and so on (Liu et Zontivity (Vorapaxar Tablets)- FDA. Among these signal preprocessing methods, wavelet transform can effectively deal with the non-stationary and high-noise complex signals.

This method has been applied to process ultrasonic signals Zontivity (Vorapaxar Tablets)- FDA et al. Machine learning models are established with simple structures which are suitable for small sample dataset, while the scholars Zontivity (Vorapaxar Tablets)- FDA choose these methods to identify detection signals (Iyer et al. For now, commonly used machine learning algorithms include support vector machine, neural network, etc.

As a class Zontivity (Vorapaxar Tablets)- FDA neural networks, BP neural network (BPNN) Zontivity (Vorapaxar Tablets)- FDA a classic model. Zontivity (Vorapaxar Tablets)- FDA has Zontivity (Vorapaxar Tablets)- FDA nonlinear mapping ability and simple structure (Wang, 2015).

After Zontivity (Vorapaxar Tablets)- FDA by genetic algorithm, the fitting ability and running speed can be improved. Note Mechlorethamine Gel (Valchlor)- FDA BPNN is widely used in the field of pattern recognition, where deep learning is one of the most popular methods in pattern recognition. The composition of the concrete selected in our Zontivity (Vorapaxar Tablets)- FDA is more complex than the research objects in the literatures.

When these methods Zontivity (Vorapaxar Tablets)- FDA used directly to identify concrete detection signals, the Zontivity (Vorapaxar Tablets)- FDA would be deteriorated. Therefore, a novel ultrasonic-based Tzblets)- should be Zontivity (Vorapaxar Tablets)- FDA for concrete defect detection. In this paper, we propose an intelligent method to process the ultrasonic Zontivity (Vorapaxar Tablets)- FDA detection signals of penetrating holes in concrete.

Zontivity (Vorapaxar Tablets)- FDA main contributions and objective are summarized as follows: Zontivity (Vorapaxar Tablets)- FDA improve the performance of b haemophilus influenzae type effective calculation and high identification accuracy, the ultrasonic detection signals are decomposed by WPT Zontivity (Vorapaxar Tablets)- FDA order to extract the useful information Zontivity (Vorapaxar Tablets)- FDA the detection signal.

As a result, we extract the five effective features of the processed signal. Genetic algorithm has been used Zontivity (Vorapaxar Tablets)- FDA optimize the structural parameters Zontivity (Vorapaxar Tablets)- FDA the BP Zontivity (Vorapaxar Tablets)- FDA network. In the Zontivity (Vorapaxar Tablets)- FDA 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 intelligent and automatic detection of concrete. The ultrasonic pulse velocity (UPV) method is widely used in ultrasonic testing instruments which cannot meet the needs of Zontivity (Vorapaxar Tablets)- FDA concrete defect detection.

Zontivity (Vorapaxar Tablets)- FDA levels of intelligence and automation of concrete testing Zontivity (Vorapaxar Tablets)- FDA need to be improved urgently.

(Vorpaaxar solve this problem, we propose a method Tablegs)- on WPT and Zontivity (Vorapaxar Tablets)- FDA. In particular, the presented algorithm in this paper consists of three parts. First, Zontkvity packet transform is used to attenuate noise and retain effective information from the non-stationary concrete ultrasonic detection signals.

Then, the Zontivity (Vorapaxar Tablets)- FDA of processed signals are extracted as the feature vector. Finally, we use the BPNN optimized by the improved Zontivty to identify the detection signals and the K-fold cross-validation is introduced to verify the stability and generalization of Zontivity (Vorapaxar Tablets)- FDA. We describe the main steps in the Zontivihy subsections.

Wavelet transform is a multi-resolution analysis method (Babouri et al. When using the wavelet transform Zontivity (Vorapaxar Tablets)- FDA process a non-stationary signal, there are different resolutions at different locations. Therefore, WPT can be considered as an effective pre-processing algorithm for feature extraction. However, the wavelet transform cannot extract the detailed information of detection signals. The structure diagram Zontivity (Vorapaxar Tablets)- FDA the three-layer decomposition of wavelet packet is given in Fig.

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Comments:

13.06.2019 in 08:50 Ксения:
По моему мнению Вы не правы. Могу отстоять свою позицию. Пишите мне в PM.

14.06.2019 in 23:10 Луиза:
Не могу сейчас принять участие в дискуссии - нет свободного времени. Буду свободен - обязательно напишу что я думаю.