Event‐triggered output synchronization for nonhomogeneous agent systems with periodic denial‐of‐service attacks
Bibliographic record
Abstract
Summary This article presents a two‐layered framework to investigate the output synchronization problem in nonhomogeneous agent systems in the presence of periodic energy‐limited denial‐of‐service (DoS) attacks with an event‐triggered control strategy. Under the developed framework, the proposed controller for different agents is composed of a dynamic compensator and a regulator, where the dynamic compensator on the first layer can copy the dynamics of the leader node. DoS attacks with a periodic (partially known) attack strategy are based on a time sequence and interrupt the data exchange over a shard network consisting of the leader node and dynamic compensators in the first layer. An event‐triggered control protocol is proposed to reduce the update frequency of the controller, and it also ensures that the output of every dynamic compensator can track the output of the leader node without occurring “Zeno phenomenon.” Based on the output regulator theory, the regulator for every agent can guarantee that all nonhomogeneous agents can converge to the trajectory of every dynamic compensator in the bottom layer. Finally, a simulation example is demonstrated to verify the new design method developed.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".