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Record W1968803934 · doi:10.1115/imece2006-16045

Modeling of the Dynamic Surface Profile in Magnetic Fluid Mirrors

2006· article· en· W1968803934 on OpenAlexaff
Azhar Iqbal, Foued Ben Amara

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicOptical Coherence Tomography Applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDeformable mirrorAdaptive opticsOpticsWavefrontDeflection (physics)Computer scienceMagnetic fieldPhysics

Abstract

fetched live from OpenAlex

Magnetic fluid deformable mirrors (MFDMs) present a simple alternative to the expensive and delicate wavefront correctors currently in use in adaptive optics (AO) systems. Such mirrors are particularly suitable for retinal imaging AO systems. The practical implementation of a retinal imaging AO system incorporating a MFDM requires an effective control system to control the shape of the mirror surface. The real-time control of the mirror surface requires a model of the mirror that can be used to determine the dynamic response of the mirror to a magnetic field applied as the control input. This paper presents such a model that not only determines the dynamic response of the MFDM but also takes into account the unique requirements of the retinal imaging application of the mirrors. The mirror is modeled as a horizontal layer of a magnetic fluid. The dynamic response of the mirror to the applied magnetic field is represented by the deflection of the free surface of the layer. The surface deflection is determined by the modal analysis of the coupled fluid-magnetic system. Considering the requirements of the retinal imaging application, the effects of surface tension and depth of the fluid layer are duly represented in the model. The mirror model is described in a state-space form and can be readily used in the design of a controller to regulate the mirror surface shape.

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: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.193

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 head0.008
GPT teacher head0.210
Teacher spread0.202 · 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

Citations0
Published2006
Admission routes1
Has abstractyes

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