Space magnetic field control based on active disturbance rejection generalized predictive control
Bibliographic record
Abstract
The combination of active magnetic compensation technology and magnetic shielding room is one of the key technologies for measuring magnetoencephalography. However, due to the non-uniform and asymmetric distribution of the residual magnetic field within the compact magnetic shielding room, the traditional active magnetic compensation system with single point feedback is limited. In this paper, a space magnetic field control for the active magnetic compensation system is firstly proposed, including six independent coils and six sensors, which can sequentially control the uniform and gradient magnetic field in three axes. Meanwhile, considering the three-axis magnetic field coupling and external disturbances of the system, the combination of active disturbance rejection generalized predictive control is designed as the control method for the system. Results show that the magnetic field uniformity has been improved by 38.2%, 42.7%, and 42.1% in the three-axis directions, respectively, compared with traditional single point feedback control, and there are significant improvements in magnetic field coupling and magnetic field disturbance suppression. This study contributes to creating a highly uniform and extremely weak magnetic field environment, promoting the development of magnetoencephalography.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".