A Linear Position Measurement Scheme for Long-Distance and High-Speed Applications
Bibliographic record
Abstract
In the maglev train electric drive system, the linear electric machine is a welcome solution. However, for long railways, the mover position measurement is an issue, especially when the train speed is very high, e.g., 600 km/h. The position measurement method of the mover must be simple and precise at high speeds while maintaining a low cost for industrial applications. The principle of the stator permanent magnet (stator-PM) machine provides evidence that the magnets and windings can be mounted on the mover of the linear resolver, leaving a simple secondary-side structure on the railway. With this idea, this article presents a PM linear resolver that generates orthogonal back-EMFs as the sinusoidal and cosine (SIN/COS) signals. The train position can then be measured by decoding the PM back-EMF. On the other hand, the high-frequency injection is imposed to solve the mover position measurement at zero speed and low speed. This PM linear resolver is prototyped with printed circuit board windings instead of regular coil windings. The dedicated decoding firmware is developed to evaluate the measurement performance up to 60 km/h in the lab setup.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".