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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".