Designing real‐time systems using imprecise discrete‐event system specifications
Bibliographic record
Abstract
Summary Real‐time (RT) systems include hardware and software components interacting in a tight fashion. Although formal methods for RT systems development have advanced, they are sometimes difficult to apply in practical applications, and scalability is compromised as the complexity of the system scales up. Instead, using modeling and simulation (M&S) methods and tools has showed to be useful for verification of practical aspects of RT systems (and having the advantage to be able to including models of the physical environment they interact with). Although several efforts exist in M&S of RT systems, none of them has considered problems of transient overloading in the RT systems specifications. Here, we introduce a new theoretical framework called I‐DEVS (imprecise discrete event systems specification) with the goal of guaranteeing responses to inputs within specified time constraints under such transient overloading conditions. The solution presented here has the advantages of a formal specification and the practicality of an M&S‐based approach. We also discuss how to define hierarchical models running in RT, and we present a set of tools that can be applied to develop RT‐embedded applications, and RT simulations.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".