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Record W3211367484 · doi:10.1115/1.4052943

Post-Processing Deflectometry Grid Images Using Particle Image Velocimetry Analysis

2021· article· en· W3211367484 on OpenAlexaff
Patrick O’Donoughue, Olivier Robin, Kévin Rouard, Alain Le Bot, Alain Berry

Bibliographic record

VenueJournal of vibration and acoustics · 2021
Typearticle
Languageen
FieldComputer Science
TopicOptical measurement and interference techniques
Canadian institutionsUniversité de Sherbrooke
FundersAgence Nationale de la Recherche
KeywordsParticle image velocimetryOpticsGridParticle tracking velocimetryVelocimetryCalibrationImage processingComputer scienceVibrationPhase (matter)PixelComputer visionAcousticsArtificial intelligencePhysicsImage (mathematics)MathematicsGeometryTurbulence

Abstract

fetched live from OpenAlex

Abstract Deflectometry is a full-field optical technique for surface slope measurement based on recording the deformation of a grid image. A hybrid method is explored in which the grid images from a deflectometry measurement are processed using a particle image velocimetry analysis tool. The hybrid approach is compared to a common phase shifting algorithm for grid images based on a windowed discrete Fourier transform. The resulting slope maps compare well with those identified using the spatial phase shifting procedure. While the traditional phase shifting method has a tuning requirement that limits the optical setup to configurations that produce an integer number of pixels per grid period in the image, the use of particle image velocimetry analysis omits this calibration step. The applicability of an existing turnkey tool to perform full-field vibration imaging using deflectometry can benefit to research concerning mechanical vibration and related experimental methods.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.553
Threshold uncertainty score0.444

Codex and Gemma teacher scores by category

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

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 head0.029
GPT teacher head0.307
Teacher spread0.278 · 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 designBench or experimental
Domainnot available
GenreMethods

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

Citations2
Published2021
Admission routes1
Has abstractyes

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