Identification of paper machines cross-directional models in closed-loop
Bibliographic record
Abstract
Paper machines cross-directional (CD) processes are a class of spatially distributed systems. Due to economic constraints, identification experiments are usually severely limited making the identification of this large dimension multi-variable model challenging. The industrial identification technique uses bump test data where a few actuators are stepped while the CD process is running in open-loop. This paper presents a technique for the identification of paper machines CD models in closed-loop. The spatial interaction matrix is replaced by a noncausal spatial finite impulse response (FIR) model to account for the actuator response in the cross-direction (CD). The non-causal FIR model is identified in a prediction error frame using least squares. Least squares identification delivers parameter uncertainty bounds that translate to bounds on the uncertainties in the spatial interaction matrix which are less than the values assumed in industrial practice. Identifying the spatial model from a rich spatial input signal provides accurate CD response models from limited scans in a low signal-to-noise ratio (SNR). The proposed techniques are illustrated by identification experiments conducted on an industrial paper machine simulator.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".