Dynamic sensor calibration: A comparative study of a Hall effect sensor and an incremental encoder for measuring shaft rotational position
Bibliographic record
Abstract
The following work investigates a cost efficient method of measuring shaft rotational position. The proposed sensor configuration consists of a magnet and a Hall effect sensor. When compared to the alternative (an optical encoder), this approach has several advantages including cost, and durability. The puck shaped magnet was placed on the end of a rotating shaft and generated a magnet field oriented transverse to the shaft axis. The Hall effect sensor was placed in a stationary holder co-axially aligned with the shaft and slightly offset, in the axial direction, from the magnet. The sensor output was compared to a high accuracy incremental encoder, which is the industry standard technique. The proposed sensor was tested for its ability to record shaft rotational speed under a variety of test conditions, including; various constant speeds, varying speeds, magnet size, sensor to magnet lateral distance, and various obstructions between the magnet and the sensor (termed readability). The sensor provided excellent measurement results, under all test conditions and compared well to the incremental optical encoder.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".