Application of Stationary Wavelet Transforms to Ultrasonic Crack Detection
Bibliographic record
Abstract
Ultrasonic-based pulse-echo technique has been widely used for non-destructive crack detection. The noisy ultrasonic signals reflected by inhomogeneous materials and other effects add difficulty to pulse echo extraction. In order to address this issue, a method is proposed in this paper to remove noise. Firstly, the raw signal is processed using stationary wavelet transform. Secondly, kurtosis is employed as a criterion to retain the appropriate wavelet coefficients on a specific scale, and to zeroize all wavelet coefficients on other scales. Thirdly, the remaining wavelet coefficients are shrunken by a soft- threshold rule using universal threshold sigmaradic2log(n), the maximum standard deviation of wavelet coefficients on all scales before being zeroized, and n is the data length. Fourthly, cross-correlation analysis between the de-noised signal and the transmitted pulse signal is conducted. Finally, the time-of-flight of the pulse in a material is measured and the flight distance is calculated. Experimental results indicate that the proposed method can detect the crack position effectively
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".