Geometry-driven petri nets and a method for modelling mechatronic control systems
Bibliographic record
Abstract
The development process of mechatronic control systems often relies on physical prototypes to test the interactions between the control software and mechanical components. However, the logistics of synchronizing a concurrent development process and the risks of integrating only partially completed sub-systems often limits effective prototyping. The consequent lack of feedback can lead to overly complex and unreliable systems which may have to undergo expensive re-designs. The interactions between mechanical systems and control software can also be recreated artificially by combining a hybrid modelling language with computer graphics technology. A dynamic 3D environment can generate sensor telemetry for input to a control system, which in turn alters the state of the environment through virtual actuators. This kind of simulation allows engineers to explore a larger design space early during the development process without committing significant resources to physical prototypes. This dissertation introduces a method for simulating mechatronic systems using Petri Nets and Scene Trees. The following chapters formally define the modelling language and illustrate the software architecture and user interface of a novel simulation development environment. The research is validated through qualitative reasoning and by demonstrating a simulation that detects design flaws in a mechatronic system which may have otherwise lead to expensive redesigns in the physical system.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".