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Record W1426457448 · doi:10.3233/jae-141860

Robust and accurate crack reconstruction for eddy current non destructive testing

2014· article· en· W1426457448 on OpenAlexaff
Min Li, David A. Lowther, Frederico Gadelha Guimarães

Bibliographic record

VenueInternational Journal of Applied Electromagnetics and Mechanics · 2014
Typearticle
Languageen
FieldEngineering
TopicNon-Destructive Testing Techniques
Canadian institutionsMcGill University
Fundersnot available
KeywordsEddy currentEddy-current testingNondestructive testingNoise (video)Inverse problemInverseCurrent (fluid)Computer scienceAcousticsStructural engineeringEngineeringMathematicsArtificial intelligencePhysicsMathematical analysisGeometryElectrical engineering

Abstract

fetched live from OpenAlex

Fast and accurate crack detection techniques are required for the eddy current non destructive testing, especially in the case where a large amount of environmental noise exists. This paper presents a robust inverse solution for accurate shape reconstruction of natural cracks. A robust formulation is employed to deal with the uncertainties caused by the noise in the eddy current testing signals. The proposed method is capable of producing more robust results than the conventional formulation in terms of the statistical analysis of the reconstruction tests.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.759
Threshold uncertainty score0.607

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 head0.018
GPT teacher head0.237
Teacher spread0.220 · 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 designTheoretical or conceptual
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
Published2014
Admission routes1
Has abstractyes

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