Timed fault-tolerant supervisory control
Bibliographic record
Abstract
In Mulahuwaish,1–4 we investigated the problem of fault tolerance in the framework of untimed discrete-event systems (DES). This approach is different from the typical fault-tolerant methodology as the approach does not rely on detecting faults and switching to a new supervisor; it requires a supervisor to work correctly under normal and fault conditions. This is a passive approach that relies upon inherent redundancy in the system being controlled. In this paper we extend the work of Mulahuwaish1–4 to the timed DES (TDES) setting. We introduce our setting, and then provide a set of timed fault tolerant definitions designed to capture different types of fault scenarios and to ensure that our system remains controllable in each scenario. As the nonblocking property is the same for timed and untimed DES, the untimed fault-tolerant nonblocking properties and algorithms from Mulahuwaish1–4 can also be used in the timed setting without any changes. We then present algorithms to verify these properties followed by complexity analyses and correctness proofs of the algorithms. An example is then provided to illustrate our approach.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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 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".