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Record W1964537730 · doi:10.1109/wcica.2014.7052950

Hysteresis modeling for IPMC actuators with rate-dependent Preisach model

2014· article· en· W1964537730 on OpenAlexaff
Ying Feng, Wirut Kumkongkaew, Juan Du, Subhash Rakheja, Chun‐Yi Su

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicDielectric materials and actuators
Canadian institutionsConcordia University
Fundersnot available
KeywordsHysteresisActuatorBendingVoltageControl theory (sociology)CurvatureNonlinear systemMaterials scienceComputer scienceMagnetic hysteresisArtificial musclePreisach model of hysteresisSmart materialComposite materialPhysicsEngineeringMathematicsElectrical engineeringCondensed matter physicsMagnetizationMagnetic fieldControl (management)

Abstract

fetched live from OpenAlex

Ionic polymer-metal composites(IPMCs) is a class of new smart materials, which can generate large bending motions under a low driving voltage, and this property of IPMCs can be used as artificial muscle driving devices without the traditional components. However, one strong non-smooth nonlinearity, hysteresis, exists between its bending curvature and the applied input voltage. The hysteresis curve is altered with the change of the input voltage rate, which causes the hysteresis in IPMCs with rate-dependent characteristics. In this paper, one modified Preisach hysteresis model is proposed to describe the rate-dependent hysteresis in IPMCs. In order to identify the effectiveness of the proposed modeling method, the comparison results between the experiment measured output and the computing output are given in this paper.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.687
Threshold uncertainty score0.539

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 head0.009
GPT teacher head0.184
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.

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

Citations5
Published2014
Admission routes1
Has abstractyes

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