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Record W2144096797 · doi:10.1109/icarcv.2008.4795525

A generalized asymmetric play hysteresis operator for modeling hysteresis nonlinearities of smart actuators

2008· article· en· W2144096797 on OpenAlexaff
Mohammad Al Janaideh, Chun‐Yi Su, Subhash Rakheja

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPiezoelectric Actuators and Control
Canadian institutionsConcordia University
FundersMichigan State University
KeywordsActuatorHysteresisControl theory (sociology)Shape-memory alloyMagnetostrictionSmart materialTopology (electrical circuits)Materials sciencePhysicsComputer scienceEngineeringCondensed matter physicsControl (management)Magnetic fieldElectrical engineering

Abstract

fetched live from OpenAlex

Smart actuators such as Shape Memory Alloy actuators, magnetostrictive actuators, and piezoceramic actuators show different symmetric and asymmetric hysteresis loops. Shape Memory Alloy actuators and magnetostrictive actuators, as an example, exhibit saturated output at maximum and/or minimum input. In this paper, a generalized Prandtl-Ishlinskii model is formulated to characterize hysteresis nonlinearities of different Smart actuators. In this model, a generalized asymmetric play hysteresis operator is proposed and integrated with a density function to characterize different asymmetric hysteresis loops of smart actuators. This modified play hysteresis operator shows the capability to generate minor and major hysteresis loops with varying slopes of ascending and descending input-output curve. Moreover, this operator exhibits saturated major and minor input-output relationships. The capability ability of the proposed model to characterize hysteresis loops of different smart actuators is demonstrated by comparing its output with measured saturated symmetric and asymmetric hysteresis loops of SMA actuators and magnetostrictive actuators.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.745
Threshold uncertainty score0.919

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.018
GPT teacher head0.214
Teacher spread0.195 · 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

Citations4
Published2008
Admission routes1
Has abstractyes

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