Calibration of Multi-Axis MEMS Force Sensors Using the Shape-From-Motion Method
Bibliographic record
Abstract
Precise calibration of multi-axis microelectromechanical systems (MEMS) force sensors is difficult for several reasons, including the need to apply many known force vectors at precise orientations at the micro- and nanoNewton (nN) force scales, and the risk of damaging the small, fragile microdevices. To tackle these challenges, this paper introduces the shape-from-motion calibration method. A new design of a two-axis MEMS capacitive force sensor with high linearity and nN resolutions is presented. Structural-electrostatic coupled-field simulations are conducted in order to optimize the sensor design. The designed sensor is calibrated with the shape-from-motion method, the least-squares method as well as the gravity-based method for comparison purposes. Calibration results demonstrate that the shape-from-motion method provides a rapid, practical, and accurate technique for calibrating multi-axis MEMS sensors
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".