The primary bottleneck to extracting wood defects during ultrasonic testing is the accuracy of identifying the wood defects. The wavelet energy moment was used to extract defect features of artificial wood holes drill...The primary bottleneck to extracting wood defects during ultrasonic testing is the accuracy of identifying the wood defects. The wavelet energy moment was used to extract defect features of artificial wood holes drilled into 120 elm samples that differed in the number of holes to verify the validity of the method. Wavelet energy moment can reflect the distribution of energy along the time axis and the amount of energy in each frequency band,which can effectively extract the energy distribution characteristics of signals in each frequency band; therefore,wavelet energy moment can replace the wavelet frequency band energy and constitute wood defect feature vectors. A principal component analysis was used to normalize and reduce the dimension of the feature vectors. A total of 16 principal component features were then obtained, which can effectively extract the defect features of the different number of holes in the elm samples.展开更多
A novel approach to extract edge features from wideband echo is proposed. The set of extracted features not only represents the echo waveform in a concise way, but also is sufficient and well suited for classification...A novel approach to extract edge features from wideband echo is proposed. The set of extracted features not only represents the echo waveform in a concise way, but also is sufficient and well suited for classification of non-stationary echo data from objects with different property.The feature extraction is derived from the Discrete Dyadic Wavlet Transform (DDWT) of the echo through the undecimated algorithm. The motivation we use the DDWT is that it is time-shift-invariant which is beneficial for localization of edge, and the wavelet coefficients at larger scale represent the main shape feature of echo, i.e. edge, and the noise and modulated high-frequency components are reduced with scale increased. Some experimental results using real data which contain 144 samples from 4 classes of lake bottoms with different sediments are provided. The results show that our approach is a prospective way to represent wideband echo for reliable recognition of nonstationary echo with great variability.展开更多
基金financially supported by the Fundamental Research Funds for the Central Universities(2572016CB11 and 2572014CB35)Natural Science Foundation of Heilongjiang Province(F2015036 and QC2014C010)948 Project(2014-4-78)
文摘The primary bottleneck to extracting wood defects during ultrasonic testing is the accuracy of identifying the wood defects. The wavelet energy moment was used to extract defect features of artificial wood holes drilled into 120 elm samples that differed in the number of holes to verify the validity of the method. Wavelet energy moment can reflect the distribution of energy along the time axis and the amount of energy in each frequency band,which can effectively extract the energy distribution characteristics of signals in each frequency band; therefore,wavelet energy moment can replace the wavelet frequency band energy and constitute wood defect feature vectors. A principal component analysis was used to normalize and reduce the dimension of the feature vectors. A total of 16 principal component features were then obtained, which can effectively extract the defect features of the different number of holes in the elm samples.
文摘A novel approach to extract edge features from wideband echo is proposed. The set of extracted features not only represents the echo waveform in a concise way, but also is sufficient and well suited for classification of non-stationary echo data from objects with different property.The feature extraction is derived from the Discrete Dyadic Wavlet Transform (DDWT) of the echo through the undecimated algorithm. The motivation we use the DDWT is that it is time-shift-invariant which is beneficial for localization of edge, and the wavelet coefficients at larger scale represent the main shape feature of echo, i.e. edge, and the noise and modulated high-frequency components are reduced with scale increased. Some experimental results using real data which contain 144 samples from 4 classes of lake bottoms with different sediments are provided. The results show that our approach is a prospective way to represent wideband echo for reliable recognition of nonstationary echo with great variability.
