Detection of internal corrosion by long-pulse thermography and digital image processing
Bibliographic record
Abstract
This paper addresses the problem of internal pitting corrosion in metals. The approach to solving this problem is carried out by means of long-pulse thermography (step heating) and digital image processing, using the median filter in the pre-processing and the Fourier transform in the processing. The long pulse thermography technique is an excellent way of detecting corrosion in metals. According to the state of the art, most of the works reviewed detect only the presence of external corrosion. In the experiments plates of different thickness were evaluated, one of 2 mm and another of 8 mm; this is another point in favor since most of the reviewed articles only focus on a single sample. In the development of this project, the following lines of research have been identified: implementation of new digital image processing methods, improvement of the thermography technique using other energy sources to heat the plates in a shorter time, exploring other functions of image processing in programming languages, carrying out tests with plates of different thickness and size, measuring the percentage of corrosion.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".