A Novel IoT-Enabled System for Real-Time Monitoring Home Appliances Using Petri Nets
Bibliographic record
Abstract
Correct use of home appliances is intended to avoid property damage and life unsafety. Also, serious fire accidents can affect the safety of one’s neighborhood. Therefore, this study uses the Internet of Things (IoT) platform to build an intelligent system that aims to detect the temperature rise of home appliances. It is employed to monitor the usage states of home appliances in real time. Hereby, the microcontroller, node microcontrol unit (NodeMCU-32S), is used to develop an IoT platform combined with sensors for measuring ac voltage, current, and ambient temperature so that the electrical power and temperature rise of load (e.g., electric motor) can be detected. Meanwhile, the smart measurement system is integrated with mobile devices to upload the detected datasets to the Google cloud database system. Moreover, the verification of feasibility and soundness of a system model is performed by using the Petri net tool, WoPeD, for the purpose of eliminating the improper states to optimize the system performance. Finally, the experimental results show that the proposed IoT-enabled system has a promising precision of 94.17% and a recall of 92.26%, which obviously outperforms other existing state-of-the-art systems.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".