Magnetic Flux Leakage Signal Processing of Ferromagnetic Material and Defect Reconstruction
Bibliographic record
Abstract
Magnetic flux leakage(MFL) testing is one of the most commonly used methods for nondestructive testing(NDT) of ferromagnetic materials.The key element is to reconstruct the defect profile based on the measured MFL signals.Both RBFNN(radial basis function neural network) and GRNN(generalized regression neural network)were proposed,by means of which two different kinds of nonlinear mapping between defect MFL signals and defect depths were respectively established.The training data samples were from the simulated data sets for 3-D finite element models while the testing data samples were from MFL testing data which were precisely interpolated by RBFNN after smooth filtering and wavelet denoising preprocessing.These two neural networks were first respectively trained to approximate the matrix of defect depth with the training data samples.Then each of them was applied to reconstruct defect with the testing data samples.The testing results demonstrated that the two neural networks could achieve 3-D imaging and visualization of defects in MFL testing,and especially GRNN was superior to RBFNN on defect reconstruction.
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How this classification was reachedexpand
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) | 0.001 | 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 teacher head, 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".