Functional Design and Development of Smart Temperature Regulating Clothing Based on Artificial Intelligence
Bibliographic record
Abstract
Intelligent thermoregulation clothing as a new type of functional clothing, the design and development of which is receiving more and more attention.PID algorithm, as a kind of classical control algorithm, realizes the precise control of the clothing temperature regulation system by adjusting the three parameters of proportionality, integration and differentiation.The control system is firstly constructed according to the principle of PID control.Then the PID controller parameters are optimized by BP neural network to improve the response speed and stability of the temperature control system.Finally, the intelligent thermoregulation garment with physical therapy and health care and portable storage is designed.Experimental verification of the parameter self-tuning PID control based on BP neural network, the BP neural network can make the temperature better maintained near the set value, the control effect is more satisfactory.The final design of the smart thermoregulation garment has a body surface temperature retention rate of 98.35% after 30 minutes at -10°C and with the heating function on.The thermal sensation evaluation of the intelligent thermoregulation garment by the subjects in different states is concentrated between "0-2", indicating that the garment can play a more ideal temperature control effect.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".