Dynamics of Mechanically Over-Constrained Inertial Sensors
Bibliographic record
Abstract
Microsystems Technology based inertial sensors offer important advantages in low-invasive measurement of spatial motion with sub-micron accuracy. Their successful implementation hinges upon achieving very low distortion and noise at the low end of the frequency spectrum. Of particular importance is the Vibration Rectification Error (VRE) — an apparent shift in the signal bias that occurs when inertial sensors are subjected to vibration. A common approach to the reduction of VRE is assuring a highly symmetrical mechanical structure of sensors. Furthermore, a low cross-axis sensitivity is desirable. In accelerometers these properties are achieved by employing multiple flexures supporting the seismic mass. However, this may lead to mechanical over-constraining and multiple local equilibria rather than a single global one. Multiple equilibria combined with the nonlinearity of flexures create conditions for chaotic behavior, which can greatly degrade the sensors’ performance. We investigate representative architectures of high performance servo accelerometers, study the impact of over-constraining, and develop comprehensive dynamic models accounting for the presence of this condition. Given the complexity of spatial motion of the proof mass and resulting deformations in the flexures, we employ computer aided generation of constitutive, symbolic and scaleable models of the investigated sensors. We illustrate analytical investigations with numerical simulations and experimental results.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".