Depth Retrieval Procedures in Pulsed Thermography: Remarks in Time and Frequency Domain Analyses
Bibliographic record
Abstract
In the present study, a Carbon Fibre Reinforced Polymer (CFRP) sample of trapezoid shape, consisting of internal artificial delaminations of various sizes and depth locations, is investigated by means of optical pulsed thermography for the retrieval of quantitative depth information. The main objectives of this work are to evaluate the produced depth estimation accuracy from two contrast-based depth inversion procedures as well as to correlate the acquired results with characteristics such as the location and size of the detected features and with analysis parameters such as the selection of the sound area. Quantitative analysis is performed in both the temporal and frequency domains, utilising, respectively, the informative parameters of thermal contrast peak slope time and blind frequency. The two depth retrieval procedures are applied for the depth estimation of features ranging in size from 3 mm to 15 mm and in depth from 0.2 mm to 1 mm. The results of the present study showed that the two different analyses provided efficient depth estimations, with frequency domain analysis presenting a greater accuracy. Nevertheless, predicting errors were observed in both cases and the factors responsible for these errors are defined and discussed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".