Electromagnet design allowing explicit and simultaneous control of minimum wire spacing and field uniformity
Bibliographic record
Abstract
Abstract An adaptive algorithm is presented that works to independently and explicitly control both local power deposition and magnetic field uniformity in a robust, automated manner. The only inputs to the algorithm are the minimum allowable wire spacing, the maximum allowable field error, and the geometry over which to produce the coil. To highlight the function of the algorithm, two examples are investigated: a transverse gradient coil insert, designed for imaging the head and neck; and a highly uniform, low‐power, B0 field coil. Both examples require highly optimized relative weighting between power dissipation and field uniformity. The algorithm is described and implemented in terms of the boundary element method for coil design. The algorithm is shown to produce acceptable designs; conforming to engineering constraints, for both instances within a matter of minutes. © 2012 Wiley Periodicals, Inc. Concepts Magn Reson Part B (Magn Reson Engineering) 41B: 120–129, 2012
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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) | 0.000 | 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 | 0.001 | 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".