A novel model reduction method for sheet forming processes using wavelet packets
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Bibliographic record
Abstract
Cross-directional control of sheet forming processes, such as a paper machine, can involve up to 600 inputs and 3000 outputs. For such large-scale systems, it is necessary to find proper model reduction strategies before starting controller design. The paper introduces a model reduction method for such processes based on an efficient modified wavelet packet algorithm. The large dimensional signals in the spatial domain can be transformed into a small number of scaling and wavelet coefficients in the wavelet domain, thus the dimension of the original input-output model is reduced without losing any significant information. Two additional benefits are obtained: (1) the system's controllability can be significantly improved because the system's condition number is greatly decreased, (2) the physical limits of the actuators can be directly transformed from the original model to the reduced model.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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