A DYNAMIC SECOND-ORDER ESTIMATION STRATEGY FOR FAULTY SYSTEMS, 1-14.
Bibliographic record
Abstract
This paper introduces a novel second-order state estimation method that is applied to linear systems dealing with modelling uncertainties.This method produces state estimates by decreasing the innovation sequence (measurement error) and its time difference which results in preserving smoothness and stability against modelling uncertainties.This filter is referred to as the second-order filter since it updates state estimates based on values of the measurement error and its incremental change.The corrective gain of this filter is designed based on a time-varying manifold that is a linear combination of the measurement error and its time difference.This manifold introduces a cut-off frequency coefficient into the filter formulation.The optimal version of the dynamic second-order filter is then calculated by finding the optimal value of this coefficient at each time step such that the state error covariance matrix is minimised.It is shown that the corrective gain of the optimal second-order filter collapses to the Kalman filter's gain for a known model with white noise.In order to verify the accuracy of the method, it is implemented on an aerospace electro-hydrostatic actuator setup under the normal and faulty scenarios.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".