Designing Optimal Thresholds for Ternary Event-based State Estimation via Multi Objective Particle Swarm Optimizer
Why this work is in the frame
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Bibliographic record
Abstract
The paper proposes a novel multi-objective approach for optimizing the threshold values in a ternary event-triggering (TET) mechanism using within event-based estimation architecture. In particular, Multi-Objective Particle Swarm Optimization (MOPSO) is employed as the optimization technique considering three objectives, i.e., the maximization of the rate of communicating quantized measurements together with the minimization of the number of idle and event epochs. In addition, the optimization process is subject to three constraints in order to guarantee the feasibility of the overall structure. The multi-objective optimizer has been utilized to automatically find the optimal design. The proposed method, referred to as the TEB-PSO, is capable of identifying a set of optimal values for the two thresholds within the TET to reduce the communication overhead. The simulation results confirm the effectiveness of the proposed method with the TET mechanism.
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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 | 0.001 |
| 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