Near-zero magnetic field disturbance suppression method based on adaptive filtering and quasi-proportional resonance control
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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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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it