Temperature Stability and Humidity on Infant Incubator Based on Fuzzy Logic Control
Bibliographic record
Abstract
Premature babies were born need to be placed inside an incubator to keep its body temperature and humidity in a certain condition. In this paper shows the design and implementation of baby incubator using intelligent control to keep the temperature and humidity. The particular incubator uses an Arduino Mega 2560, an Arduino Uno, an DHT22 Sensor, and an LM35 Sensor. Fuzzy Logic Control has implemented inside the Arduino Mega 2560 to keep the maximum overshoot oscillations and to keep the error signal under 5%. The desired temperature is around 36°C and the humidity around 80% to 60% RH value. The research is conducted in two sessions, one without a load and one with 2 Kg load to simulate the weight of a Baby. The testing result of incubator without load has achieved the stability level which it is quicker than with 2 kg load. Overall the maximum overshoot and the signal error on both research accomplished with the set goal is under 5%.
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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 | 0.000 | 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".