Controller Synthesis of Time Petri Nets Using Stopwatch
Bibliographic record
Abstract
Scheduling is often a difficult task specially in complex systems. Few tools are targeted at both modeling and scheduling of the systems. In controller synthesis, a scheduler is seen as a controller to manage shared resources and timing requirements of a system. This paper proposes a time Petri net-based approach for controller synthesis and finding a scheduler using stopwatch. The solution suggested here is particularly interesting for preemptive scheduling purposes. This paper deals with time Petri nets with controllable and uncontrollable transitions and assumes that a controllable transition can be suspended and retrieved when necessary. In fact, the paper supposes that every controllable transition can be associated with stopwatch. With this hypothesis, the objective is to model a system by time Petri nets and calculate subintervals where the system violates the given property. Then, the controller associates the corresponding controllable transitions with stopwatch to suspend them in their bad subintervals. The interesting advantage of this solution is that this approach synthesizes an ordinary time Petri net model before adding stopwatch. Therefore, complicated computations and overapproximations required during controller synthesis of time Petri nets associated with stopwatch are avoided.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".