Offline and Online Parameter Learning for Switching Multirate Processes With Varying Delays and Integrated Measurements
Bibliographic record
Abstract
It is difficult to measure properties of certain key/quality variables at a fast rate due to technical constraints or economical considerations. Such variables are measured infrequently through laboratory analysis with a considerable delay. Also, sample collection for laboratory analysis may be extended over a significant time interval. In this article, the objective is to solve the parameter estimation problem along with real-time output prediction for switching multirate sampled processes with unknown varying delays and unknown varying sampling intervals. First, under the framework of the expectation–maximization (EM) algorithm, offline parameter estimation problem of dual-rate switching augmented regression models is handled. The delays, sampling intervals, and operating modes are considered as the hidden variables modeled by a nonparametric-distribution-based approach. In addition, as the fast-rate prediction of the slow-rate sampled variables is often used for process control applications, a recursive EM algorithm is used to predict the fast-rate outputs in real time. The efficacy of the proposed algorithms is shown through an experimental study on a laboratory hybrid tank system. The results show a satisfactory fast-rate prediction of the slow-rate sampled variables, and the significance of considering different sampling intervals is highlighted. Also, estimates of the occurrence probabilities of each possible delay, sampling interval, and mode are obtained without being limited by the assumption of a prior distribution.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".