Application of the Mechatronic Design Quotient (MDQ) for Intelligent Design and Evolutionary Design of Mechatronic Systems
Bibliographic record
Abstract
Mechatronic systems are integrated electro-mechanical systems. Intelligent mechatronic systems possess computational intelligence with capabilities such as perception, learning, reasoning, and making inferences from incomplete information. A mechatronic system will consist of many different types of interconnected components and elements. The dynamic coupling between components means an accurate design of the system should consider the entire system as a whole rather than using single-criterion and sequential design methodologies, which are traditional. However, in view of the system complexity, it is difficult to adopt a "wholistic" approach in practice. The presentation will explore a multi-criteria and concurrent approach to mechatronic design and evaluation. A design formulation and criteria based on the concepts of mechatronic design quotient (MDQ) will be introduced for this purpose. Human experience on mixed systems and interactions between criteria will be taken into account by applying techniques of soft computing for the aggregation of criteria. The use of artificial intelligence and evolutionary computing in the design of mechatronic systems may be viewed as an attempt to mimic "natural" intelligent design and "natural" evolution of a biological system (e.g., human), albeit in a greatly simplified form. The talk will address these concepts as well. In particular, intelligent design is applicable when human intelligence is used in the design process. On the other hand, evolutionary design is applied when evolutionary computing is used in the design process. Several industrial applications of intelligent mechatronics have been designed and developed in the Industrial Automation Laboratory under the direction of the speaker.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".