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Record W2051920293 · doi:10.1088/0960-1317/19/7/074020

Fast step-response settling of micro electrostatic actuators operated at low air pressure using input shaping

2009· article· en· W2051920293 on OpenAlexaff
Lukas Mol, L.A. Rocha, Edmond Cretu, R.F. Wolffenbuttel

Bibliographic record

VenueJournal of Micromechanics and Microengineering · 2009
Typearticle
Languageen
FieldEngineering
TopicAdvanced MEMS and NEMS Technologies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSettlingSettling timeActuatorMaterials scienceControl theory (sociology)MechanicsAcousticsEngineeringElectrical engineeringPhysicsControl engineeringComputer scienceStep responseEnvironmental engineering

Abstract

fetched live from OpenAlex

Squeeze-film damping is highly inadequate in low-pressure systems or in systems where air pressure and/or gap dimensions are poorly defined. Input shaping has been used to circumvent the oscillations typically associated with under-damped mass-spring-damper systems and drastically decrease the settling time. The proposed method does not rely on feedback but solely on the system dynamics. The required input signal is derived analytically from the differential equation describing the system. The resulting device response is simulated and experimentally verified on an electrostatically actuated microstructure. Settling occurs even faster than for an equivalent critically damped system.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.007
GPT teacher head0.211
Teacher spread0.204 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2009
Admission routes1
Has abstractyes

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