State estimation via Markov switching‐channel network and application to suspension systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The problem of estimation for a class of networked non‐linear systems is investigated. A practical scenario with multiple switching communication channels coexisting in the network is considered. System signals are exchanged over the multiple communication channels and each channel is subject to the two main transmission imperfections, network‐induced time‐varying delays and packet dropouts. The channel switching is assumed to be governed by a continuous‐time Markov process, and a Markov jump non‐linear system model is exploited to represent the overall networked system. Linear estimators are designed such that the underlying estimation error system is stochastically stable and the disturbance rejection attenuation satisfies an performance bound. As a case study, a state estimation problem for an intelligent active suspension system is addressed to verify the theoretical findings.
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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 | 0.001 | 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 it