A Moving Window Formulation for Recursive Bayesian State Estimation of Systems with Irregularly Sampled and Variable Delays in Measurements
Bibliographic record
Abstract
The time delay involved between sampling and obtaining measurements of certain quality variables is a common scenario in various process applications. Further, this delay is not fixed and can vary for various reasons. Moreover, certain measurements may be sampled at irregular time intervals. The state estimation algorithms available in the literature have been developed for the scenario where the measurements are sampled regularly or are available after a fixed time delay. In this work, a recursive moving window Bayesian state estimator formulation is proposed to utilize such measurements with variable time delays to compute the state estimates. The length of the moving window ensures that the algorithm utilizes all the available measurements (delayed or otherwise) for computing the state estimates. In practice, it may also become necessary to account for the physical bounds on the states. A constrained version of the moving window recursive state estimator is also developed to yield state estimates that are consistent with their respective bounds and constraints. The efficacy of the unconstrained moving window state estimator is demonstrated by application on the benchmark Tennessee Eastman simulation case study and an experimental two-tank heater−mixer setup, while the efficacy of the constrained moving window state estimator is demonstrated by simulation of a benchmark gas-phase batch reactor system.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".