Optimal fast-rate soft-sensor design for multi-rate processes
Bibliographic record
Abstract
Measuring accurate parameters and states at fast rates in some systems may have a significant cost associated with it or it may even be unfeasible. Soft-sensors are a good substitution in these cases. This paper studies the problem of optimal soft-sensor design for multi-rate processes. The main idea is to extend the Kalman filter to the multi-rate case to design a Kalman filter based soft-sensor. The state lifting method is introduced that can be easily used to generalize the minimum variance Kalman filtering method to the multi-rate case for fast-rate estimation. The optimal Kalman gains and covariance matrices are found at fast rate, based on multi-rate input-output data and fast-rate system models. Some examples, especially the one taken from a real mechanical system for air-fuel ratio control, validate the applicability of the proposed method to soft-sensor design in dual-rate and multi-rate processes represented in the state-space form
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".