Roles of Perception in Engineering Design – A Theoretical Foundation to Improve Designer’s Performance
Bibliographic record
Abstract
Engineering design is a complex decision-making process which frames the transition from an engineering problem to a final product, to meet a set of requirements. During this process, perception is inevitably involved. Perception, a term originated from psychology, referring to a process where a person arrives at an interpretation of his/her sensory experience about the surroundings, has been involved in a broad scope in engineering design, e.g., understanding a design problem, comprehending customers requirements, conceptualizing design thoughts, organizing and managing resources, and evaluating alternative solutions. To study the engineering process from the perception’s perspective, a theoretical model has been proposed. In this model, workload, skill, knowledge, and affect are chosen as major factors. Based on the model and the Environment-Based Design methodology, methods are proposed to quantify designer’s perception and performance at conceptual design stage. Experimental studies have been conducted to validate the proposed model. As a result, the model serves well as a phase-based quantification tool for designer’s perception and performance. In addition, a significant positive correlation has been found between one’s perception and performance. Furthermore, the model implies a foundation to improve one’s performance for engineering design.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".