Structural and instrumentation design of a microelectromechanical systems biaxial accelerometer
Bibliographic record
Abstract
Microscale biaxial accelerometers are required to be sensitive to applied accelerations and manufacturable by means of microelectromechanical systems technology. In order to meet these requirements, a compliant realization of biaxial accelerometers, dubbed simplicial biaxial accelerometers, has been proposed, as reported in this paper. Notched joints, to realize what is termed Π-joints in the parallel-robots literature, are employed and then improved by the introduction of Lamé-shaped hinges serving as flexible joints. The sensitivity of the simplicial biaxial accelerometers in estimating accelerations is investigated and validated by means of finite element analysis. The sensing system is embedded in the simplicial biaxial accelerometers, with piezoresistive sensing technology adopted in the instrumentation design. Using the principles of piezoresistive sensing, the electronic layout is developed for the accelerometer. Through the piezoresistive analysis implemented on the finite element model of the simplicial biaxial accelerometers, the matrix that maps voltage signals into acceleration signals is derived. By virtue of both the structural and electronic designs, the accelerometer is observed to be sensitive to accelerations in its plane, but fairly insensitive to accelerations in any of the other four directions of the rigid-body motion. Finally, prototypes were fabricated with microelectromechanical systems technology to test the microfabrication feasibility of the structure and measurement system of the accelerometer. Test results are the subject of a forthcoming paper.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".