Practice of Cognitive Function and Computational Model Fusion in Smart Product Design Optimization
Bibliographic record
Abstract
In order to improve the design of intelligent products, user cognition and perceptual engineering are integrated into intelligent product design.And through subjective survey and physiological measurements and other techniques to measure the user's emotional experience of the product, to construct the user's emotional evaluation model based on BP neural network.Multimodal interaction technology is used to optimize the product design method, and the implicit needs of users for intelligent products are obtained through the method of multimodal perception, which is matched with the product interaction, so as to propose the intelligent product design strategy based on multimodal interaction.In order to verify the effect of the strategy, physiological indicators and perceptual imagery are obtained to evaluate the products.Finally, the user satisfaction of intelligent products under this strategy is studied.The benefit ratio of the smartwatch designed based on the design strategy of this paper (0.438811) is better than other market competitors.The user satisfaction of the 10 experience dimensions of this smartwatch is distributed in the range of [80%, 93%], the dimension with the highest satisfaction is functionality, the lowest is attractiveness, and the overall satisfaction is 86.6%, and the smartwatch designed by this paper's design strategy obtains a high level of user satisfaction.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".