Management of Heterogeneous Information for Integrated Design of Multidisciplinary Systems
Bibliographic record
Abstract
Multidisciplinary systems (such as Mechatronics or Cyber Physical Systems) are considered as the resulting integration of design expertise from several disciplines such as electrical/electronic, mechanical and computer sciences. As a result, a large number of design data, such as software code, CAD models, 0D/1D and 2D/3D CAE results, etc. are generated by designers and heterogeneous computer-based tools throughout the whole development process. Therefore, effective exchange between designers from different disciplines is required in order to achieve multidisciplinary integration. In order to insure knowledge sharing between the designers issued from the different disciplines, the heterogeneous information from previous design projects could be captured, elucidated and managed by designers. In this paper, after presenting the multidisciplinary integration during the system design, the importance of effective exchange between designers from different disciplines is highlighted. Then, the existing techniques related to the capture and management of heterogeneous information are presented. Afterwards, an approach helping designers to capture the design data issued from CAD models and 0D/1D and 2D/3D CAE results, is introduced. Finally, the conclusion is drawn and future work is pointed out.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".