A Novel Smart Gas Stove with Gas Leakage Detection and Multistage Prevention System Using IoT LoRa Technology
Bibliographic record
Abstract
The outline and implementation of a low power real-time gas leakage detection and LoRa wireless communication technology-based notification system are accompanied by the multistage safety features in the kitchen as well as in the house have been inaugurated in this paper. The proposed system comprises a LoRa client and a LoRa gateway. LoRa client was made by the RFM69HW LoRa module, Arduino Uno, and some sensing devices, which were installed in the kitchen. Primarily a LoRa gateway was installed in our community. This LoRa gateway was associated with a cloud server (Ubidots) by employing Wi-Fi networks as transmission media. When gas leakage was detected, liquid crystal display (LCD), and the buzzer were activated, thereupon a GPS sensor identified the geographical position of the affected area, and LoRa client stored the measured data to Ubidots IoT platform, afterword data was sent to the user and police station, and eventually, the main power circuit at home was tripped off, and the exhaust fan was activated for avoiding further accidents. Here, for the efficient use of heat from a conventional gas stove in Bangladesh, and subsequently, for the secure power supply to the whole system, power was generated from the unused heat during cooking using the seeback concept of thermoelectric power generation(TEG) module.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".