Detection method of multiple oscillations based on wavelet analysis

2013 ◽  
Author(s):  
Guo Zixu ◽  
Xie Lei ◽  
Shi Minghua
Keyword(s):  
Wavelet Analysis ◽  
Detection Method

A Detection Method for Traffic Incident Using Wavelet Analysis

2000 ◽  
Vol 33 (9) ◽  
pp. 241-245
Author(s):  
Shoufeng Ma ◽  
Guoguang He ◽  
Guizhu Wang
Keyword(s):  
Wavelet Analysis ◽  
Detection Method ◽  
Traffic Incident

scholarly journals Detection of Fusarium Head Blight in Wheat Ears Using Continuous Wavelet Analysis and PSO-SVM

Agriculture ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 998
Author(s):  
Linsheng Huang ◽  
Kang Wu ◽  
Wenjiang Huang ◽  
Yingying Dong ◽  
Huiqin Ma ◽  
...  
Keyword(s):  
Wavelet Analysis ◽  
Fusarium Head Blight ◽  
Detection Method ◽  
Support Vector ◽  
Spectral Features ◽  
Continuous Wavelet ◽  
Head Blight ◽  
Detection Model ◽  
Svm Algorithm ◽  
Wavelet Features

Fusarium head blight, caused by a fungus, can cause quality deterioration and severe yield loss in wheat. It produces highly toxic deoxynivalenol, which is harmful to human and animal health. In order to quickly and accurately detect the severity of fusarium head blight, a method of detecting the disease using continuous wavelet analysis and particle swarm optimization support vector machines (PSO-SVM) is proposed in this paper. First, seven wavelet features for fusarium head blight detection were extracted using continuous wavelet analysis based on the hyperspectral reflectance of wheat ears. In addition, 16 traditional spectral features were selected using correlation analysis, including two continuous removal transformed spectral features, six differential spectral features, and eight vegetation indices. Finally, wavelet features and traditional spectral features were used as input features to construct fusarium head blight detection models in combination with the PSO-SVM algorithm, and the results were compared with those obtained using random forest (RF) and a back propagation neural network (BPNN). The results show that, under the same feature variables, the PSO-SVM detection method gave an overall higher accuracy than the BPNN detection method, while the overall accuracy of the RF detection model was the lowest. The overall accuracy of the RF, BPNN and PSO-SVM detection models with wavelet features was higher by 3.7%, 2.9% and 8.3% compared to the corresponding methodological models with traditional spectral features. The detection model with wavelet features combining the PSO-SVM algorithm gave the highest overall accuracies (93.5%) and kappa coefficients (0.903) in the six monitoring models. These results suggest that the PSO-SVM algorithm combined with continuous wavelet analysis can significantly improve the accuracy of fusarium head blight detection on the wheat ears scale.

Voltage Sag Detection Method Based on Wavelet Information

2015 ◽  
Vol 740 ◽  
pp. 470-473
Author(s):  
Da Hai Zhang ◽  
Cheng Yu Ge ◽  
Chen Chen ◽  
Xian He Han
Keyword(s):  
Wavelet Analysis ◽  
Power Quality ◽  
Detection Method ◽  
Voltage Sag ◽  
Major Power ◽  
Wavelet Method ◽  
Quality Problem ◽  
Local Maxima ◽  
Multi Scale ◽  
Voltage Sag Detection

Voltage sag is the major power quality problem and receives wide attention. Although wavelet analysis works well for detecting voltage sag features, the existence of noise can reduce the advantages of wavelet method or even make it ineffective. To solve the problem, the paper uses multi-scale wavelet information by multiplying the results of several scales, and then searches the local maxima from the product to find the transition moment of voltage sag. The proposed method can suppress the noise and improve the accuracy for detecting voltage sag features. Simulation result validates the effectiveness of the proposed method.

Leak Detection Method for Long Pipeline Based on Dynamic Pressure and Wavelet Analysis

2010 ◽  
Author(s):  
Bin Xu ◽  
Likun Wang ◽  
Hongchao Wang ◽  
Min Xiong ◽  
Dongliang Yu ◽  
...  
Keyword(s):  
Wavelet Analysis ◽  
Pressure Sensor ◽  
Static Pressure ◽  
Detection Method ◽  
Detection System ◽  
Dynamic Pressure ◽  
Field Tests ◽  
Leak Detection ◽  
Analysis Method ◽  
System A

Architecture of the leak detection system is presented, and the leak detection method based on dynamic pressure and wavelet analysis is studied in this paper. The feature of dynamic pressure which is generated by the leakage of pipeline is analyzed. The dynamic pressure signal of pipeline internal pressure is acquired by dynamic pressure sensor when leakage occurs, and the signal is analyzed by wavelet analysis method to detect the singularity, and the singularity is used to recognize and locate the leak. From the comparison of analysis results between dynamic pressure and static pressure, in order to improve the sensitivity and stability of the leak detection system, a polling rule between dynamic and static pressure is implemented. Field tests of the leak detection system are presented and analyzed. The results of the field tests demonstrate that the leak detection method based on dynamic pressure and wavelet analysis can detect pipeline leak rapidly and locate the leak precisely. This leak detection system has been applied in 5000 km pipeline or so.

Structural Crack Detection Using Hilbert-Huang Transform Method and Wavelet Analysis

2011 ◽  
Vol 105-107 ◽  
pp. 710-713
Author(s):  
Long Liu ◽  
Da Shan Dong
Keyword(s):  
Signal Processing ◽  
Wavelet Analysis ◽  
Crack Detection ◽  
Detection Method ◽  
Narrow Band ◽  
Cantilever Beams ◽  
Screening Process ◽  
Transform Method ◽  
Hilbert Huang Transform ◽  
Structural Cracks

Crack detection, Hilbert–Huang transform, Wavelet analysis Abstract. This paper illustrates the feasibility of the Hilbert–Huang transform (HHT) as a signal processing tool for detecting structural cracks. HHT is a promising method to extract the properties of nonlinear signals. However the HHT has some shortcomings, for example some undesirable pseudo-IMFs and multi-component IMF. The crack detection method based on the wavelet analysis and HHT is proposed to analyze response signals: the wavelet analysis is used to decompose the signal into a set of narrow band signals prior to the HHT. Then, a screening process is conducted to remove unrelated IMFs. By comparing the IMFs’ instantaneous amplitudes, the existence of structural crack is detected. Finally, the experiment results of the cantilever beams prove the reliability of this method.

A structure damage detection method based on wavelet analysis and type-2 fuzzy logic system

2014 ◽  
Author(s):  
Sy Dzung Nguyen ◽  
Quang Thinh Tran ◽  
Kieu Nhi Ngo ◽  
Xuan Phu Do ◽  
Seung-Bok Choi
Keyword(s):  
Fuzzy Logic ◽  
Wavelet Analysis ◽  
Damage Detection ◽  
Detection Method ◽  
Fuzzy Logic System ◽  
Structure Damage ◽  
Logic System
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