Simplectic Architectures for True Multi-axial Accelerometers: A Novel Application of Parallel Robots
Bibliographic record
Abstract
Several triaxial accelerometers are known. However, to the knowledge of the authors, no true, triaxial accelerometers are commercially available. By true we mean an accelerometer which would pick up the three components of point-accelerations using one single proof-mass. What we propose is novel architecture classes of parallel-kinematics-machine for multi-axial accelerometers, that is, accelerometers that can measure n components of point-accelerations, where n = 1, 2, 3. We call these architectures simplectic, as they use n + 1 legs oriented normally to the n + 1 faces of the regular simplex associated with the n-dimensional subspace of measured acceleration components. We show that the simplectic biaxial accelerometer can be fabricated using micromachining MEMS techniques, while the simplectic triaxial accelerometer lends itself to compliant-mechanism fabrication techniques. CAD models of the prototypes proposed are provided for all of the three novel mechanical architectures proposed. Finally, the direct kinematics problems associated with the simplectic biaxial and triaxial accelerometers are shown to be linear in both cases. This feature simplifies the estimation of the proof-mass displacement from piezoresistive or piezoelectric measurements taken at the flexible joints connecting the legs of the mechanism to the rigid body whose acceleration is under estimation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".