Guidelines for automated implementation of executable object oriented models for real-time embedded control systems
Bibliographic record
Abstract
We present our experiences in applying real time scheduling theory to embedded control systems designed using ROOM (Real time Object Oriented Modeling) methodology. ROOM has originated from the telecommunications community and has been successfully applied to many commercial systems through the supporting case tool ObjecTime. It is particularly suitable for modeling reactive real time behavior. Furthermore, it provides many other advantages through the use of object orientation, and the use of executable models from which code may be generated quickly and efficiently. Since many real time embedded control systems have significant reactive, event driven behavior, it is attractive to use ROOM methodology to develop such systems. However, the ROOM methodology does not provide tools to specify and analyze the temporal behavior as is required for the hard real time components of embedded systems, and for which the real time scheduling theory provides an analytical basis. We show how real time scheduling theory may be applied to ROOM models using a cruise control example to illustrate. The biggest challenge comes from minimizing the adverse effects of priority inversions. Our results are very encouraging, and we show that not only is it possible to apply real time scheduling theory, but that it can be done very efficiently provided certain guidelines are followed in the design and implementation of the ROOM model.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".