State Estimation for Multirate Measurements in the Presence of Integral Term and Variable Delay
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Measurements in the process industry can arrive with fast or slow sampling rates. Fast measurements, such as flowrate and temperature, are sampled frequently and are obtained instantly after sampling. The slow measurements, which are usually related to chemical quality variables such as product composition, are sampled infrequently and have some delay due to laboratory analysis. Moreover, sample collection for laboratory analysis may extend over a significant time interval, and the slow measurements are actually functions of all the states during the sampling period. Our objective is to develop a multirate state estimation method for this situation. We propose two methods to solve the problem: the exact Bayesian approach and the augmented state approach. In the exact Bayesian approach, the algorithm is developed by implementing the Bayes rule. In the augmented state approach, the system is reformulated by augmenting the current state with past information required for fusing the slow measurements and then applying general state estimation procedures. The performance of the proposed methods for state estimation is demonstrated through simulation and experimental studies and by comparison with methods that only use the fast measurements.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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