Latent Low Rank Representation Applied to Pulsed Thermography Data For Carbon Fibre Reinforced Polymer Inspection
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper explores the implementation of Latent Low-Rank Representation (LatLRR) on pulsed thermographic data. LatLRR decomposes an image in the form of a linear association of three types of information: observed, unobserved and noise. This information is then used in order to separate the salient and principal features. This study has found that when used as a post-processing method prior to the application of state-of-the-art signal processing techniques, such as principal component thermography (PCT) and pulsed phase thermography (PPT), LatLRR significantly improves defect detection: 18% for PCT and 92% for PPT. Nevertheless, no noticeable improvement was measured when LatLRR was used to reconstruct a noiseless version of each image of a dataset, before processing it with a state-of-the-art algorithm. The investigations conducted on each type of feature returned by the LatLRR have also failed to provide results regarding the detection of defects.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it