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Record W2028892506 · doi:10.1109/ultsym.2014.0014

Multi-layer ultrasonic imaging for non-destructive testing applications

2014· article· en· W2028892506 on OpenAlexaff
Nasim Moallemi, Shahram Shahbazpanahi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicGeophysical Methods and Applications
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsBeamformingLayer (electronics)Signature (topology)Computer sciencePoint (geometry)Ultrasonic sensorAcousticsMaterials sciencePhysicsTelecommunicationsGeometryNanotechnology

Abstract

fetched live from OpenAlex

In a multi-layer medium, interfaces between the layers refract the sound wave into lower layers, thereby playing the role of emitting sources for lower layers. We model the interfaces between the layers of a multi-layer medium as spatially distributed sources consisting of infinite number of point sources. Then, we use this model to develop a new model for array spatial signature for any point inside a multi-layer medium. This new model can be used in any imaging technique. In this paper, we present the application of our new model for array spatial signature in conventional beamforming algorithm in order to image the second layer of a two-layer medium.

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: Methods · Consensus signal: none
Teacher disagreement score0.934
Threshold uncertainty score0.379

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 head0.025
GPT teacher head0.286
Teacher spread0.261 · 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
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

Citations0
Published2014
Admission routes1
Has abstractyes

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