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A Robust Autoparametrically Excited Angular Rate Sensor

2021· article· en· W3187105698 on OpenAlexaff
Bhargav Gadhvi, Farid Golnaraghi, Behraad Bahreyni

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMechanical and Optical Resonators
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsCapacitive sensingAmplitudePhysicsBandwidth (computing)Nonlinear systemResonance (particle physics)Inertial frame of referenceExcited stateCapacitanceElectrodeAtomic physicsElectrical engineeringOpticsComputer scienceEngineeringClassical mechanicsQuantum mechanicsTelecommunications

Abstract

fetched live from OpenAlex

We report, for the first time, a robust angular rate sensor that is operated at 2:1 Autoparametric Resonance (AR) with a wide frequency bandwidth at 3dB amplitude drop of 320.7 Hz. The sensor utilizes inherent forcing and inertial or elastic nonlinearities arising from electrostatic forces and fabrication imperfections respectively, to excite the sense mode via 2:1 AR causing a wider frequency response of the sense mode. The sensor is actuated electrostatically, and its output is sensed using variable gap capacitive electrodes. The sensor is tested on a rate table and a maximum scale factor of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$38.99\ \mu \mathrm{V}/^{\circ}/\mathrm{s}$</tex> with a full-scale nonlinearity of 1.2%, dynamic range of ±270 °/s, and noise density of 0.042 °/s/√Hz are measured.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.767
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0110.000

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 head0.023
GPT teacher head0.234
Teacher spread0.210 · 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 designTheoretical or conceptual
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

Citations0
Published2021
Admission routes1
Has abstractyes

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