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Record W2063565163 · doi:10.1063/1.4914638

Dynamic ECA lift-off compensation

2015· article· en· W2063565163 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAIP conference proceedings · 2015
Typearticle
Languageen
FieldEngineering
TopicNon-Destructive Testing Techniques
Canadian institutionsQuébec Metro High Tech Park (Canada)
Fundersnot available
KeywordsLift (data mining)CalibrationEddy-current testingElectromagnetic coilEddy-current sensorComputer scienceFalse positive paradoxEddy currentElectronic engineeringAcousticsEngineeringElectrical engineeringArtificial intelligencePhysicsData mining

Abstract

fetched live from OpenAlex

Good control on lift-off is crucial in Eddy Current Testing (ECT) as the signal amplitude, directly affected by lif-toff changes, can potentially lead to reduced detection performance and/or false positives. This is especially true in automated inspections with Eddy Current Array (ECA) technology, where lift-off cannot be mechanically compensated for at each coil position. Here, we report on a novel method for compensating sensitivity variations induced by varying lift-off for an ECA probe. This method makes use of a single ECA probe operated in two different ways: One is to create a set of detection channels and the other is to create a set of lift-off measurement channels. Since a simple relationship exists between the two measurements, an improved calibration process can be used which combines the calibration of both detection and lift-off measurement channels on a simple calibration block exhibiting a reference indication, thus eliminating the need for a predefined lift-off condition. In this work, we will show results obtained on a weld cap, where lift-off condition is known to vary significantly over the scanning area.

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.

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 score0.404
Threshold uncertainty score0.824

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 head0.033
GPT teacher head0.258
Teacher spread0.225 · 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