Application of Sparse Non-Negative Matrix Factorization in infrared non-destructive testing
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
Non-negative matrix factorization (NMF) solves the problem of negative basis in principal component analysis (PCA) and widely used in diverse applications in different fields. Here, we show an application of sparse-NMF in infrared non-destructive testing (IR-NDT) imaging. We applied Sparse-NMF to determine the subsurface defects of an Aluminium plate specimen applying active thermographic method. To obtain results we compared the ability of Sparse-NMF to detect subsurface defects and its computational load in compared to state-of-the-art thermographic approaches such as: principal component analysis/thermography (PCT), Candid Covariance-Free Incremental Principal Component Thermography (CCIPCT), Sparse PCT, non-negative matrix factorization (NMF), and standard NMF with gradient descend (GD) and nonnegative least square (NNLS). The results show considerable performance (93%-39.52s) for Sparse-NMF, which conclusively indicate the promising performance as a confirmation for the outlined properties.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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