Pulse shaping in infrared thermography for nondestructive evaluation
Bibliographic record
Abstract
Infrared thermography is a common technique for nondestructive evaluation. In active infrared thermography privileged here, a thermal stimulation is required to generate relevant thermal contrasts on specimens. Among stimulation techniques, pulse heating is one of the most common along with lock-in thermography. In this article, a technique is discussed that shows that by modifying the pulse heating shape, higher thermal contrasts are generated (for a given level of energy injection). In fact, it was found that ideally, it is better to use two pulses separated by a short Δ time interval. Variation of Δ directly influences the thermal contrast. By increasing Δ, the thermal contrast first improves up to a certain level before to reduce below the reference value (single pulse case). This was tested on several materials of low, medium, and high thermal conductivity. Moreover, this was also confirmed by an appropriate thermal model (not discussed in the article). In the text, a theory and experiments are provided.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".