Automatic Clothes Retriever (ACR)
Bibliographic record
Abstract
As a tropical country, Indonesia is located below the equator and has sunshine all year round, the sun's rays have been used to dry wet clothes after people wash their clothes. However, with climate change happening, the current weather is difficult to predict. there are many chances of clothes getting wet due to unexpected rain. Especially now that people are busy at work and clothes are hung unattended. To solve this problem, many projects have been carried out, such as standalone clothes drying system, web-based drying clothes system, and the most advanced is clothes drying system that recycle AC heat waste as source of heat. In this paper, automatic clothes retriever (ACR) equipped with mobile apps-based monitoring system was made. Using the ESP32 as the main controller, LDR, rain sensor, limit switch, motor, fan and heating lamp, the ACR will automatically recognize the weather and retrieve-out or retrieve-in the hanger to protect clothes from unexpected rain. As a result, ACR managed to respond to weather change, by retrieveinning the clothes hanger in 14.82 seconds with 10 Kg clothes on it. During the experiment, the maximum energy consumption of ACR was measured, which is 0.289KWh in one hour with full load in the system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".