A spin tracing algorithm applied to mini-type magnetic bearing meter and its SoC implementation
Bibliographic record
Abstract
Due to its large mechanical structure, the standard magnetic-flux-gate bearing-based meter cannot be housed in the very small space available on some small mobile machines. Although the semiconductor magnetic-effect-based bearing meter has miniaturizing potential, its longterm temperature stability and its azimuth measurement accuracy are not as good as those of the magnetic-flux-gate bearing meter. To solve the problem of miniaturizing the dimension of the magnetic-flux-gate-based bearing meter, this paper presents a spin-tracing algorithm (STA) developed to study a mini-type magnetic bearing meter. First, an orthogonal projection vector is structured by analyzing the magnetic bearing sensor's working principle. A theoretic expression is derived which can be used to control the tracing computation process of getting azimuth data by exploiting the triangle transform. The structure and the stability of the STA algorithm are also discussed in the paper. Furthermore, a relation between the tracing step length /spl Delta//spl beta/ and the maximum computation step number N/sub max/ at any sample is set up. The STA algorithm has been implemented as a system-on-chip based on an FPGA. Finally, the experimental data from the mini-type magnetic bearing meter based on the STA algorithm as well as its specifications are given. The experimental results show that the STA algorithm is not only efficient in miniaturizing the size of the meter, but also is excellent in improving upon the system accuracy which is two times higher than that of the old meter.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".