Sinusoidal Noise Reduction from Eddy Current Data of Steam Generator Tubes Using Iterative Weighted Multipoint Interpolated DFT (WMIpDFT) Approach
Bibliographic record
Abstract
Eddy current testing is a non-destructive technique, which is widely utilized to detect and monitor degradation and flaws such as cracks in the steam generator tubes located in nuclear power plants. Typically, the analysis of eddy current testing data of steam generator tubes is complex due to the presence of noise sources in the data. These noise sources decrease the signal to noise ratio, and therefore, the detection of flaw signals becomes a difficult task. A signal processing technique has been proposed in this study, which utilizes the Weighted Multipoint Interpolated Discrete Fourier Transform algorithm iteratively, to reduce low frequency sinusoidal noise as a noise source. The performance of the proposed algorithm was assessed based on the noise level, and the flaw signal power. It has been shown that the algorithm effectively estimates the parameters of sinusoidal noise, and improves the signal to noise ratio of the flaw signals.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".