Fuzzy Decision Making for Conceptual Design of a Visual Servoing System Using Mechatronic Multi-Criteria Profile (MMP)
Bibliographic record
Abstract
Mechatronic systems are of increased importance in engineering and their relevance goes hand in hand with the increasing complexity of the tasks they perform. Due to the inherent complexity of mechatronic systems, a concurrent systematic and multi-objective design thinking methodology is crucial to replace the often used sequential design approach that tends to deal with the different domains separately. In this research we present a new multi-criteria profile (MMP) for mechatronic system performance evaluation in the stage of conceptual design. Based on the assessed MMP for each of generated design concepts and using a method of aggregation for interacting criteria, a global performance index will be calculated. Best mechatronic configurations are determined by maximizing this performance index. In the presented paper two nonlinear fuzzy integrals called 2-additive Choquet and Sugeno will be used for the aggregation of criteria and fitting the intuitive requirements for decision-making in the presence of interacting criteria. Finally, the effectiveness of the proposed design method alongside each of the decision making models will be validated via a case study of designing a robotic visual servoing system. The comparative simulation results for the overall system performance will also be presented for both cases of using Choquet and Sugeno integrals.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".