Achieving Online Coordination in Real-Time Collaborative Assembly Modeling: A Supervisory Control Approach
Bibliographic record
Abstract
A real-time collaborative assembly modeling process involves the teamwork of multiple designers. Without adequate coordination, this multi-user based modeling process could be more time consuming, or even divergent, than the conventional single-user-based process. This paper thus presents a supervisory control approach to achieving online operational coordination of the multi-user based assembly modeling process. In this approach, we treat the real-time collaborative modeling process as a discrete-event system (DES) and then obtain an effective coordinator for the process control via the supervisory control theory (SCT). Our work extends the framework of SCT to this new application so that the assembly modeling operations and its desired operational behaviors can be modeled and controlled by a set of automata. With them, we further propose a modular supervision approach to find a group of modular supervisors. These supervisors compose the online coordinator to enforce the control specifications and to yield a nonblocking controlled process. The results show much promise for SCT in the new application domain of collaborative CAD (CCAD).
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".