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Record W2742138939 · doi:10.1109/tie.2017.2733449

A Microwave Ring Resonator Sensor for Early Detection of Breaches in Pipeline Coatings

2017· article· en· W2742138939 on OpenAlexaff
Mohammad H. Zarifi, Sameir Deif, Mohammad Abdolrazzaghi, Bertie Chen, Dennis Ramsawak, Michael Amyotte, Nahid Vahabisani, Zaher Hashisho, Weixing Chen, Mojgan Daneshmand

Bibliographic record

VenueIEEE Transactions on Industrial Electronics · 2017
Typearticle
Languageen
FieldEngineering
TopicMicrowave and Dielectric Measurement Techniques
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMiniaturizationMaterials scienceResonatorPlanarMicrostripMicrowaveOptoelectronicsBandwidth (computing)CoatingAcousticsElectronic engineeringElectrical engineeringEngineeringComputer scienceTelecommunicationsNanotechnology

Abstract

fetched live from OpenAlex

A planar microwave resonator sensor is designed, customized, and fabricated to detect coating breaches in industrial steel pipelines. The sensor, which utilizes a ring-shaped resonator to maximize the sensitivity at its core, is tuned to 2.5 GHz with a quality factor of 280. In the setup, the sensor is grounded to a piece of steel pipeline with an Epoxy-100 coating, which provides the substrate beneath the microstrip structure. It is demonstrated that any change in the gap height between the substrate layer and the pipeline, from 0 to 3.5 mm, produces a significant resonant frequency variation and bandwidth change in the sensor's response. The sensor structure demonstrates sensitivity and selectivity to air and water penetration to the breach. The sensor structure described in this work is a compact, low-cost solution and has potential for further miniaturization in mobile applications which may serve as a method for pipeline breach detection.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.046
GPT teacher head0.251
Teacher spread0.206 · 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 source (direct Gemma or distilled Codex), 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

Citations150
Published2017
Admission routes1
Has abstractyes

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