A Mean-Rate Event-Triggered Mechanism for Nonlinear Plants With Weak Time Regularization
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Bibliographic record
Abstract
This article proposes a new event-triggered mechanism for nonlinear plants with multiple communication networks. An auxiliary signal that contains a component to measure the (weighted) mean changing rates of measurement errors is employed and, hence, the induced event-triggered mechanism is termed as a mean-rate one. By incorporating this auxiliary signal into dynamic event-triggering conditions, the new mean-rate event-triggered mechanism can ensure closed-loop input-to-state stability with respect to external disturbances. To further improve transmission performance, a concept of weak time regularization is given and proved, where similar to traditional time regularization, no events can be triggered within a user-specified lower bound, but differently, the positiveness of this user-specified parameter is not necessary in excluding Zeno behavior. Finally, a nonlinear example is simulated to illustrate the feasibility and efficiency of the theoretical results.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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