Product form optimization algorithms for computer-aided industrial design instruction
Bibliographic record
Abstract
Based on the concept of "user-centered", this paper designs a product form optimization model based on ant colony algorithm.Through mining the online reviews of the products, we determine the perceptual imagery of users, and categorize the perceptual imagery and determine the weights from the perspective of user satisfaction.Combining the factor analysis of perceptual imagery and the contribution value of morphological features on perceptual imagery, the product morphology optimization fitness function is constructed.Solve the model according to the basic principle of ant colony algorithm, and study the decision-making method to assist product optimization.Take a brand A model forum word-of-mouth data as an example to analyze, obtain users' perceptual imagery through SO-PMI algorithm, and assign values to perceptual intention weights with the help of cluster analysis.Determine the contribution value of morphological features through the SD investigation of product morphological differences.Genetic algorithm is introduced to carry out comparative experiments to verify the superiority of ant colony algorithm in optimizing model solving.Finally, the application effect of the predictive model solving scheme is analyzed through user satisfaction survey.The results show that the output of the product optimization design model based on ACO algorithm Model A is 8. 23.11% of the users are very satisfied with the optimized Model A, 65.55% of the users are satisfied, and 85.72% of the survey respondents are very willing and ready to buy the optimized Model A.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".