Research on Acoustic Emission Signal Acquisition and Acoustic Source Identification of Tank Floor Corrosion
Bibliographic record
Abstract
Based on the acoustic emission source location theory of tank bottom, the sensor located among the medium inside the tank is put forward, which could identify the acoustic source of tank bottom corrosion defects together with the sensors which are arranged on tank outside wall near tank bottom, and thus the reliability of acoustic emission source location technology on the tank bottom can be increased. Location experiment of simulative tank bottom, attenuation characteristic experiment of simulative tank and acoustic source identification experiment of simulative tank bottom corrosion have been conducted. The results present that the method of acoustic source identification and location could enhance the identification of acoustic source in arbitrary triangle location, increase the area of the tank bottom zone location, decrease the missing signals caused by acoustic attenuation, at the same time, the sensors inside tank are more sensitive to acoustic source of corrosion than that outside tank, and reduce the influence caused by weather and other factors on acoustic source location. So the method can improve the reliability of acoustic emission source identification and location on tank bottom, provide theory and experiment foundation for acoustic emission testing evaluation of tank bottom. Key words: Tank bottom; Acoustic emission; Positioning; Identification
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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 | 0.001 |
| 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 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".