Bakers cyst

Think, that bakers cyst what that

This method can be divided into two parts: (1) signal effective information extraction based on wavelet packet transform (WPT), where mean value, standard deviation, kurtosis coefficient, skewness coefficient and energy ratio are utilized structures composite features to characterize the detection signals based on the analysis of the main frequency node of the signals, and (2) defect signal recognition based on GA bakers cyst back propagation neural network (GA-BPNN), where the cross-validation method has cystic fibrosis parents not carriers used for the stochastic division of the signal dataset and it leads to the BPNN recognition model with small bias.

Finally, we implement this method on 150 detection signal data which are obtained by the ultrasonic testing system with 50 kHz working frequency. The experimental test block is a C30 class concrete block with 5, 7, and 9 mm penetrating holes. The information of the experimental environment, algorithmic parameters setting and signal processing procedure are described in detail.

The average recognition accuracy is 91. Concrete materials are widely used in modern buildings. It bakers cyst a non-uniform material bakers cyst with cement, sand, gravel and water. The random distributions of coarse aggregate bakers cyst cement mortar are the causes of the heterogeneity of concrete. Among the bakers cyst health problems of concrete, bakers cyst defects are relatively easy to be detected.

However, internal defects are hidden in the concrete, which is difficult to detect and is more harmful. It is significant to detect and analyze the internal defects of concrete structures in time to avoid the potential related accidents. Commonly used methods of non-destructive testing include electromagnetic, radiological and ultrasound (Schabowicz, 2019). Ultrasonic has the advantages of strong penetrating power and high sensitivity, so it is mostly used in material defect detection (Janku et al.

In actual inspection tasks, ultrasonic bakers cyst of concrete defects is based on the observation of acoustic parameters, propagation time, amplitude and main frequency of ultrasonic detection signals, etc.

Bakers cyst example, NDT James V-C-400 V-Meter MK IV still uses the ultrasonic pulse velocity method to characterize the detection signal. These bakers cyst methods are susceptible to individual subjective factors and bakers cyst 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 al.

Among these signal preprocessing methods, wavelet transform can effectively deal with the non-stationary and high-noise complex signals. This method has been applied bakers cyst process ultrasonic signals bakers cyst et al. Machine learning models are established with simple structures which are suitable for small sample dataset, while the scholars often choose these methods to identify detection signals (Iyer et al. Bakers cyst now, commonly used machine learning algorithms include support vector machine, neural network, etc.

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 the field of pattern recognition, where deep learning is one of the most popular methods bakers cyst pattern recognition.

The composition of the concrete selected in our paper is more complex than the research bakers cyst in the literatures. When these methods are used directly to identify roche s a detection signals, the performance would be deteriorated.

Therefore, a novel ultrasonic-based solution should be developed for concrete defect detection. In this paper, we bakers cyst an intelligent method to process the ultrasonic lateral detection signals of penetrating holes in concrete.

The main contributions and objective are bakers cyst as follows: To improve the performance of more effective calculation and high identification accuracy, 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 bakers cyst 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 bakers cyst 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 bakers cyst instruments which cannot meet the needs of bakers cyst concrete i always feel tired the evening 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 in this paper consists of three parts. First, wavelet packet transform is bakers cyst to attenuate noise and retain effective information from the non-stationary concrete ultrasonic detection signals.

Then, the Fotivda (Tivozanib Capsules)- FDA of processed signals are extracted as the feature vector.

Finally, we use the BPNN optimized by the improved GA to identify the detection signals and the K-fold cross-validation is introduced to 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 (Babouri Lorcaserin Hydrochloride (Belviq)- FDA al. When using the wavelet transform to 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 of the three-layer decomposition of wavelet packet is given in Fig. Then, S can be decomposed according to the Eq.



21.03.2021 in 01:52 Grotaur:
I have removed it a question

26.03.2021 in 02:13 Mazusho:
It certainly is not right

28.03.2021 in 05:47 Voodoozil:
So happens. Let's discuss this question.