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Record W2770893675 · doi:10.1109/jmems.2017.2764272

Exclusion of Linear Acceleration Signal in the MEMS Thermal Gyroscope

2017· article· en· W2770893675 on OpenAlexafffund
Jamal Bahari, Carlo Menon

Bibliographic record

VenueJournal of Microelectromechanical Systems · 2017
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
KeywordsAccelerationGyroscopeVibrating structure gyroscopeSIGNAL (programming language)Rotation (mathematics)PhysicsMicroelectromechanical systemsAngular accelerationAccelerometerSymmetry (geometry)Control theory (sociology)Computer scienceClassical mechanicsOptoelectronicsMathematics

Abstract

fetched live from OpenAlex

This letter identifies the source of linear acceleration signal in the microelectromechanical systems (MEMS) thermal gyroscope and provides a real-time solution to exclude it. The main culprit of the undesired acceleration signal is found to be lack of rotational symmetry due to Manhattan sensor topology. A higher level of symmetry is obtained by constructing a hybrid gyroscope using two individual devices operating in tandem but 180° out-of-phase. A precision rotary stage is used to test the duo. The experiments confirmed that higher symmetry is promising in excluding the acceleration signal. Compared with a single device, the hybrid gyroscope demonstrated 16-fold reduction in the acceleration signal and 5-fold improved acceleration to rotation sensitivities.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.019
GPT teacher head0.261
Teacher spread0.241 · 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 source (direct Gemma or distilled Codex), 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

Citations4
Published2017
Admission routes2
Has abstractyes

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