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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".