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Record W4320018431 · doi:10.1109/tcns.2023.3244108

A Mean-Rate Event-Triggered Mechanism for Nonlinear Plants With Weak Time Regularization

2023· article· en· W4320018431 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Transactions on Control of Network Systems · 2023
Typearticle
Languageen
FieldEngineering
TopicStability and Control of Uncertain Systems
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNonlinear systemRegularization (linguistics)Control theory (sociology)Computer scienceEvent (particle physics)Stability (learning theory)MathematicsArtificial intelligenceControl (management)

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.991
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.204
Teacher spread0.193 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it