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Record W2995029785 · doi:10.1016/j.ifacol.2019.11.725

Piezoelectric Actuators Characterization for Simultaneous Force and Displacement Self-Sensing by detecting Impedance

2019· article· en· W2995029785 on OpenAlexaff
Bafumba Liseli Joël, Zarif Mansour Sepehr, Seethaler Rudolf, Agnus Joël, Micky Rakotondrabe

Bibliographic record

VenueIFAC-PapersOnLine · 2019
Typearticle
Languageen
FieldEngineering
TopicPiezoelectric Actuators and Control
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersAgence Nationale de la Recherche
KeywordsMechanical impedancePiezoelectricityActuatorDisplacement (psychology)Electrical impedanceSensitivity (control systems)Characterization (materials science)PreloadMaterials scienceAcousticsMechanical engineeringEngineeringElectrical engineeringPhysicsElectronic engineeringNanotechnology

Abstract

fetched live from OpenAlex

This paper introduces a new Self-Sensing Actuation approach for simultaneous estimation of external loads and Piezoelectric Actuator (PEA) displacement at a frequency where the PEA’s impedance change presents good sensitivity to both electrical and mechanical excitations. Experiments are conducted for static operations to support these claims and to help demonstrate the dependence of the PEA’s mechanical and electrical properties on external excitations: mechanical stresses with a low preload (less than the recommended 15 MPa for dynamic operations) and electric fields.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.993
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.002
GPT teacher head0.177
Teacher spread0.175 · 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 designSimulation or modeling
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
Published2019
Admission routes1
Has abstractyes

Explore more

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