Design and development of a self-contained and non-invasive integrated system for electricity monitoring applications
Bibliographic record
Abstract
This paper reports the design and integration of a self-contained system intended for real-time monitoring of electricity usage within residential and commercial buildings. Consisting of an Energy Harvester (EH), an electric current sensor, a Micro-controller Unit (MCU), and a wireless communication device, the proposed system is self-powered and non-invasive, which provides a promising solution as a node in a wireless sensor network. In this work, a customized interface circuitry is designed to collect and regulate the energy from the EH. A Wireless MCU is programmed to acquire, process, and transmit the data from the sensor to the central hub via Bluetooth Low Energy connectivity. The real-time data obtained can be used to measure the amount of power consumed by individual appliances within the building. In this paper, the system design and testing results are reported. Attached to an electric wire carrying currents in the range of 7.6A to 30A, the state-of-the-art system can achieve a read-transmit duty cycle from <;1min to 2.5min. The unit is capable of reading a 60 HZ AC sensor signal with a peak voltage in the range of 100 mV to 900 mV with an accuracy of 91.4%.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 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".