Modeling and Application of Rain-Light Sensor in Automatic Clothes Drying Design
Bibliographic record
Abstract
The development of science and technology has experienced such rapid growth, thus encouraging humans to try to overcome all problems that arise around them and ease the work around the environment through automatic control systems.One of the technologies currently being developed is the Arduino microcontroller.Based on the problems often experienced by students when leaving clothes to dry in the dormitory.If it rains or bad weather, it can be a problem if you do not have time to lift the clothesline so that dry clothes become wet with rainwater when there are no dorm residents because they are on campus.Given these problems, the author designed an automatic drying room prototype using a rain sensor and light sensor based on an Arduino microcontroller.The components used are a drying room and roof, a light sensor and water sensor as input, an Arduino microcontroller, breadboard and jumper cables, a power bank as a processor, and a servo motor and LED lights as output.A servo motor automatically moves the roof of the clothes' drying room, and the LED lights turn on, according to input from the light sensor and rain sensor, according to the conditions received by the sensor.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".