IoT-based Automatic LPG Refilling and Leakage Detection System
Bibliographic record
Abstract
Liquefied Petroleum Gas is commonly called LPG. LPG became an unavoidable one in day-to-day life. The enormous applications include domestic applications like heating and cooking purposes and industrial applications like vehicle propellant and refrigerant. Misuse of LPG will lead to the heavy disaster including life and property. The leakage of gases will produce enormous damage to human life and property. Identification of leakage is difficult because the gases are not visible to human eyes. To ease the identification of gas leakage and to take precautionary measures, a system is designed. This system will automatically sense the gas leakage and alert the user using the LCD available. Additional safety measures like automatic electricity cut-off and opening of the exit doors are available in the proposed system. The system also monitors the room temperature and the status of the temperature parameter can be seen on the LCD. The automatic cylinder refilling alerts LPG, Temperature, Sensors, Processors, GSM, AWS server system in the proposed method eases the user. The cylinder weight is calculated using the load cell and it is continuously monitored. When the weight is reduced below the pre-defined level, an alert message will be given to the user and a message to the refilling unit. A web page is developed and the user can log in using the username and password. The web page provides the current status of temperature, gas, and load cell value.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".