Modal Characterization of MEMS Switches via Output Only and Input/Output Identification Methods
Bibliographic record
Abstract
MEMS devices typically have moving or oscillating mechanical parts, and characterization of their dynamics, including their modal parameters, is highly desirable. This paper is concerned with experimental implementation of a Stochastic Subspace Identification (SSI) algorithm as well a base excitation based identification algorithm for experimental modal analysis of a micro-cantilever switch. A white noise signal applied to the built-in electrostatic actuator in the switches excited a response measured using microscanning Laser Doppler Vibrometry (LDV). In the case of identification via the SSI, only the output response was used while the base excitation based algorithm employed the input and the output signals. The modal parameters found using MACEC matched well with those predicted by theory, and the results obtained via the two experimental identification approaches are in good agreement, thus providing confidence in using the SSI approach for experimental modal analysis of MEMS structures.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 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".