Design of Gas Leakage Monitoring System Based on Android Application and NodeMCU ESP8266
Bibliographic record
Abstract
Gas leakage is a serious problem that can threaten public safety and health and cause significant material losses. This research aims to design and implement a gas leak monitoring system that can be accessed remotely based on Android applications and NodeMCU ESP8266. NodeMCU ESP8266 is chosen as the main microcontroller equipped with an MQ-2 gas sensor to detect the presence of hazardous gas. This research incorporates Internet of Things (IoT) technology to allow users to remotely monitor the condition of gas leaks, thereby increasing the level of safety in the use of gas in households or small industries. The hardware design includes the use of an MQ-2 gas sensor that is sensitive to certain gas concentrations, as well as setting up an ESP8266 NodeMCU to transmit detection data to a Firebase server for later access through an Android application. The Android application was developed using Android Studio with a focus on an intuitive user interface to monitor the status of gas leaks in real-time. he research methods used include system design, hardware and software implementation, and thorough system testing by simulating gas leak scenarios to test the reliability and response of the system. The results show that the system can detect gas leaks with high accuracy. It is expected that the system developed in this research can be a practical and effective solution in reducing the risk of accidents caused by gas leaks, as well as making a positive contribution in increasing awareness of the safety of gas use in the community.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".