Bibliographic record
Abstract
ABSTRACT Sensor fault detection and isolation in multi‐agent systems (MAS) with uncertain dynamics and undirected, connected communication networks is addressed in this article. The proposed approach involves a two‐step process: First, fault detection, and then likelihood‐based fault isolation. A novel fault reconstruction technique is introduced by tuning the unscented Kalman filter (UKF) noise covariance matrices within the Q ‐learning framework. This adjustment helps reconstruct the uncertain states of the MAS and train the internal parameters of a neural network using historical measurements. This innovative method is referred to as Enhanced reinforced UKF (ERUKF). To reduce neural network approximation errors, a robust control term utilizing the hyperbolic tangent function is applied. The stability of ERUKF, when combined with the robust control method, is mathematically proven using the Lyapunov theorem. Simulations illustrate that ERUKF exhibits lower estimation errors compared to adaptive UKF, achieving a 96.67% success rate in fault isolation under Monte Carlo (MC) simulations.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".