High stability magnetic field compensation method based on ACAC-DOB
Bibliographic record
Abstract
Abstract Magnetocardiography (MCG) measurement technology is a rapidly developing biomagnetic detection technology in the field of cardiac function imaging. However, its sensitivity to weak magnetic fields from the heart makes it susceptible to disturbances, necessitating a high-precision and stable environment due to stringent signal-to-noise ratio requirements. The existing combination of passive shielding and active compensation is hampered by the inaccuracies of the magnetic shielding room model. A high-stability magnetic field compensation method based on an all-coefficient adaptive control combined with a disturbance observer (ACAC-DOB) is introduced in this article. ACAC acquires the high-order characteristic parameters of the system using recursive least squares on a characteristic model, even in the absence of an accurate model, while DOB detects external disturbances. Experimental results demonstrate that ACAC-DOB can maintain stable control of the magnetic field with an accuracy that is 52.5% higher than that of PID, particularly when the model undergoes changes. Within the frequency range of 1–15 Hz, ACAC-DOB surpasses both ACAC, effectively compensating for external disturbances to within 20 pT and creating a low-noise environment for MCG, thereby enhancing signal-to-noise ratios.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".