Real-time simulations: effective real-time simulations of event-based systems
Bibliographic record
Abstract
This paper presents a set of novel tools that allow the efficient simulation, at fixed time steps, of event-based dynamic systems. The so-called RT-Events library is an innovative toolbox that can be used with the Simulink™ graphical software and that solves the following two problems encountered in the simulations of event-based systems: (1) time consuming variable-step algorithms; and (2) inaccurate real-time simulations with fixed-step algorithms. One important application of the new RT-Events toolbox is its capability to effectively simulate automotive systems as real-time, hardware-in-the-loop systems. It is shown that the simulations performed with the new tools are more efficient than the conventional algorithms. In particular, the important problem of reset walk, which is inherent to the classical fixed-step simulation of event-based systems, is explained and its solution obtained with the use of the blocks of the new toolbox is demonstrated. Numerical examples illustrate the effectiveness of the new simulation tools.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".