Sinusoidal Parameter Estimation and Application to Eddy Current NDT Data Records
Bibliographic record
Abstract
The general problem of assessing eddy current data from steam generators is complicated due to the presence of random noise and low frequency sinusoidal interference signals.The low frequency sinusoidal interference originates from periodic variations in the material properties or dimensions caused by manufacturing processes, behaviour of the component of the inspection system such as probe wobbling.The estimation of the sinusoidal parameters is obtained by using the cyclic-MUSIC algorithm which is a combination of two well known algorithms, Relaxed and MUSIC algorithms.The removal process generates waveforms from the estimated parameters and subtracts them from the original data.Application to several data records showed that using all the estimated parameters with two sinusoidal waveforms is the best fit due to the nature of the data.Moreover, the performance assessment was based on power measurements and correlation functions. 5.3.3Using Tube Average Frequency and Magnitude for the Whole Data Record ....................
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".