Diagnosability Test for Timed Discrete-Event 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
In this paper, an algorithm with polynomial time-complexity is presented for testing failure diagnosability in (untimed) discrete-event systems in a state-based framework. Furthermore, an algorithm for testing failure diagnosability in timed discrete-event systems is provided. The test for timed discrete-event systems, in particular, first gathers and complies the information about the timing of events (represented in the timed transition graph of the timed system) in the transition-time function of a reduced model, and then uses this model to verify diagnosability. Sufficient conditions are obtained under which the transition-time sets can be represented as the union of a bounded number of intervals, and the test will have polynomial complexity. This new test, as shown using examples, may significantly reduce the computations of testing diagnosability, compared with other polynomial diagnosability tests (for untimed systems) adapted for timed systems
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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