Implementation of the kernel techniques of real-time process algebra
Bibliographic record
Abstract
This paper presents the work on developing a code generator that automatically generates C++ code based on RTPA specifications of system architecture and behaviors. The traditional sequential part of RTPA specification can be mapped onto standard C++ counterparts or their combinations. However the important real-time features of RTPA, such as interrupt, concurrency, duration, and event/time-driven cannot be dealt with the standard C++ library. In order to implement the real-time functions of RTPA, we proposed a framework which facilitates automatic C++ code generation from RTPA specifications by two phases: the first phase processes the concrete syntax of RTPA specifications resulting in the corresponding abstract syntax trees; the second phase generates code from the abstract syntax trees. In the second phrase, the RTPA runtime library is introduced to provide real-time supporting code. This paper discusses the implementation of RTPA real-time features using real-time kernel techniques to guarantee timeliness and predictability. The hardware interrupt vectoring and interrupt service routines are used to implement RTPA interrupt, timing, and duration. The multitask-scheduling techniques are used to implement RTPA concurrency. Intertask communications are adopted to implement event/time-driven processes. Experimental case studies show the real-time features of RTPA can be achieved by this 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".