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Record W2895155795 · doi:10.1109/lsens.2018.2874062

Topology: A Source of Nonlinearity in the MEMS Thermal Accelerometer

2018· article· en· W2895155795 on OpenAlexafffund
Jamal Bahari, Carlo Menon

Bibliographic record

VenueIEEE Sensors Letters · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvanced MEMS and NEMS Technologies
Canadian institutionsSimon Fraser University
FundersCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsLinearityAccelerometerAccelerationRotation (mathematics)PhysicsConcentricNonlinear systemSymmetry (geometry)Topology (electrical circuits)ThermalGravitational accelerationProof massNatural convectionMicroelectromechanical systemsGravitationMechanicsOpticsComputational physicsConvectionGeometryElectrical engineeringClassical mechanicsEngineeringOptoelectronicsMathematicsThermodynamics

Abstract

fetched live from OpenAlex

This article reports the effect of package and constituent geometries on the response linearity of a micromachined single-axis thermal accelerometer. To investigate this phenomenon, a packaged device is filled with a high-density gas inside a sealed chamber on a rotation stage, and its response is measured as its sensitive axis of acceleration is tilted relative to the Earth's gravitational acceleration g = 9.81 m/s <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . Linearity of the response is examined compared to an analytical model describing natural convection between horizontal heated concentric cylinders. In a 360° turn, full-scale nonlinearities of 3.1% and 0.9% are measured in each half cycle. This asymmetrical performance is attributed to a lower level of geometrical symmetry in the packaged device. To compensate for the modulated response, a geometrical correction factor is introduced to the model, improving its performance prediction with a symmetrical full-scale linearity error of 0.5%.

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 categoriesnone
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 score0.016
Threshold uncertainty score0.300

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)0.0000.000

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 head0.016
GPT teacher head0.240
Teacher spread0.223 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations3
Published2018
Admission routes2
Has abstractyes

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