Bibliographic record
Abstract
This paper presents a practical wavelet-based approach to denoising coarsely quantized signals. Such signals can arise from the status data collected within large-scale engineering plants employing traditional limit checking fault detection and identification (FDI). Transitioning such plants to more advanced FDI techniques requires that the coarsely quantized data be accurately denoised. As FDI by its nature is concerned with the analysis of nonstationary signals, wavelets offer an appropriate denoising framework. Existing techniques for optimal wavelet denoising presuppose Gaussian noise contamination and, hence, are suboptimal for coarsely quantized signals. In this paper, a secondary correction stage is added to the standard wavelet-denoising process to improve its denoising performance on coarsely quantized signals. This correction stage exploits a priori knowledge of the known coarsely quantized signal dependencies to "tune" the wavelet thresholds. The effectiveness of the approach is demonstrated through the analysis of real-world data collected from an operational large-scale engineering plant
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".