Multi-Objective Optimization Applied to the Matching of a Specified Torque-Speed Curve for an Internal Permanent Magnet Motor
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Bibliographic record
Abstract
Traditionally, the objective function used for optimizing the design of an internal permanent magnet motor (IPM) has maximized efficiency or torque for a particular current or volume. Creating a particular torque speed curve can be considered to be a multi-objective problem and would usually be expressed in terms of a single objective by minimizing the average error over the curve. In this paper, it is proposed to treat this directly as a multi-objective problem thus allowing the designer to decide which tradeoffs in the torque-speed performance are most acceptable after the analysis has been performed rather than before. Additionally, a new variant of a multi-objective evolutionary algorithm using mixed elitism, intended for this problem, is described.
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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 | 0.001 | 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