Bibliographic record
Abstract
The smooth variable-structure filter is a model-based state estimation algorithm, which can be applied to linear and smooth nonlinear dynamic systems. In this algorithm, the source of uncertainty can be explicitly defined. Furthermore, convergence of the algorithm can be guaranteed, given an upper bound on the level of noise and uncertainties. Performance of the algorithm can be improved through refining the upper bounds on parameter variations or uncertainties. The filter innovation vector or (output) estimation error can be used as a measure of performance for estimation algorithms. The smooth variable-structure filter benefits from using a secondary set of performance indicators in addition to the innovation vector. Such indicators reflect the effect of the corresponding modeling errors on each estimated state or parameter. Aiming at robustness against uncertainties, the internal model used in the filtering algorithm can be dynamically refined based on the multiple performance indicators. In fault diagnosis and prognosis, the two features of dynamic model refinement and robustness against uncertainty are of critical importance. Furthermore, the smooth variable-structure filter can be combined with different Bayesian filters in order to achieve a trade-off between robustness and optimality. The reviewed applications of the smooth variable-structure filter include multiple target tracking, battery state-of-charge estimation, and robotics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.007 | 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".