Adaptive velocity estimation for disk drive head positioning
Why this work is in the frame
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Bibliographic record
Abstract
With the rapid increase of data areal density in disk drives, the need for more accurate position sensing and velocity estimation techniques for the disk drive head actuator emerges. This paper studies the application of velocity estimation methods for disk drive head positioning servo-mechanism with a focus on adaptive windowing velocity estimation. The adaptive windowing technique requires no prior knowledge of measurement noise and shows a better performance compared to conventional finite difference method and Kalman filtering technique. We have compared the performance of adaptive velocity estimation methods under study over a noisy position trajectory in terms of measures for error statistics and undesired shifting. The undesired shifting measure has been developed to reflect the estimation delay and outliers. The simulation results show the superiority of adaptive windowing velocity estimation to conventional methods.
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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 | 0.000 | 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