Near-zero magnetic field disturbance suppression method based on adaptive filtering and quasi-proportional resonance control
Bibliographic record
Abstract
Abstract The cardiac magnetic field used for magnetocardiographic (MCG) imaging must be detected in a stable near-zero magnetic field environment. In the hospital environment, there are mainly two kinds of magnetic field disturbances that affect the signal-to-noise ratio of cardiac magnetic field detection. One is the magnetic field disturbance with high power spectral density at a specific frequency, and the other is the random magnetic field disturbance with low frequency. To suppress magnetic field disturbances, this paper proposed a near-zero magnetic field disturbance suppression method that combined a PI controller with adaptive filtering and quasi-proportional resonance control (PI-APF-QPR). The magnetic field disturbance with high amplitude and specific frequency was extracted by the adaptive filter (APF) and suppressed by the quasi-proportional resonance (QPR) controller. Additionally, the low-frequency random disturbance was suppressed by the PI controller. The experimental results showed that compared with the PI controller, the peak-to-peak value of the magnetic field by the PI-APF-QPR controller was reduced by 39.1%, and the suppression ratio of the magnetic field noise by the PI-APF-QPR controller was improved by 29.5%, which verified the effectiveness of the proposed magnetic field disturbance suppression method.
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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) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 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".