Name
Abstract | ||
|---|---|---|
The wavelet packet decomposition was made for the ultrasonic testing signal of wood defects. The wavelet function of Db5 was applied to make the three-layer wavelet packet decomposition for the wood defects. Four characteristic parameters of wave form BX wave crest BF, energy distribution EF and energy percentage E were extracted in the nodes of Layer 3. The effective evaluation standard was established on the basis of characteristic information extraction. The separability of different defects in time domain eigenvector and frequency domain eigenvector was compared and analyzed respectively. The frequency domain eigenvector with better separability was served as the recognition eigenvalue of classifying defects sorts. BP neural network was used to identify the extracted frequency domain eigenvector, and the total recognition rate reached to 83.3%. Therefore, the method presented in the study is feasible in the wood defects recognition. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/EMEIT.2011.6023236 | EMEIT |
Keywords | Field | DocType |
frequency domain eigenvector,bp neural network,ultrasonic testing signal,forestry,wavelet transforms,inspection,time domain eigenvector,frequency-domain analysis,backpropagation,three-layer wavelet packet decomposition,ultrasonic materials testing,wood processing,acoustic signal processing,feature extraction,time-domain analysis,energy distribution,wood defect recognition,wave crest,signal characteristic extraction,information extraction,signal classification,db5 wavelet function,energy percentage,wavelet packet transform,eigenvalues and eigenfunctions,wave form,neural nets,ultrasonic testing,neural network,frequency domain analysis | Control theory,Artificial intelligence,Artificial neural network,Wavelet packet decomposition,Eigenvalues and eigenvectors,Wavelet,Wavelet transform,Frequency domain,Time domain,Pattern recognition,Feature extraction,Speech recognition,Engineering | Conference |
Volume | Issue | ISBN |
2 | null | 978-1-61284-087-1 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Huimin Yang | 1 | 1 | 1.06 |
| Lihai Wang | 2 | 9 | 4.33 |
| Yumei Wang | 3 | 0 | 2.03 |