Vers la gestion globale des interactions « commande-diagnostic ». Analyse et synthèse
Bibliographic record
Abstract
Cet article aborde le problème de la commande tolérante aux défauts (FTC pour Fault Tolerant Control) pour les systèmes multivariables. La méthodologie proposée est fondée sur le développement d'un schéma qui fédère le module de diagnostic (FDI pour Fault Detection and Isolation) et le mécanisme de tolérance, tout en conservant la loi de commande déjà en place (i.e. la commande nominale). L'un des intérêts de l'approche proposée réside dans le fait que les performances nominales en fonctionnement normal sont entièrement préservées. La méthodologie est posée dans un contexte H∞ dit de « sensibilité mixte » et utilise la valeur singulière µ et sa généralisation µg pour extraire le meilleur couple FDI/FTC pour une application et un cahier des charges donné. L'exemple de simulation HiMAT illustre la mise en œuvre des différentes étapes de synthèse.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".