Artificial Intelligence Based Analysis System for Standard Energy Meter Current Measurement Module
Bibliographic record
Abstract
This paper proposes to design the power meter based on TMR current sensor, screen the chips that meet the requirements of the power meter, and stipulate the technical specifications and technical parameters of the power meter based on TMR current sensor.Design the system structure of power meter with TMR current sensor including MCU module, storage module, communication module and so on.And design the main and vice system clocks in the single-phase energy meter with TMR current sensor.Analyze the design of signal acquisition module, bias adjustment and temperature compensation module, communication module and circuit protection module in the current monitoring system.According to the characteristics of the TMR sensor, establish the objective function, improve the GWO algorithm, and optimize the design of the multi-stage magnetic ring structure current sensor.The performance parameters of the TMR sensor are analyzed, and the DC current test and AC current test are conducted to verify the performance of the TMR current sensor measurement module.The accuracy, precision and linearity of the current measurement module are tested, and the relative error between the actual current value and the theoretical current value derived from the formulae in the DC current test and the AC current test are controlled within 5% in the TMR current measurement system.The measurement system based on TMR current sensor meets the current measurement requirements.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".