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Record W1969849815 · doi:10.1109/jlt.2011.2179523

A Distributed Pressure Measurement System Comprising Multiplexed In-Fibre Bragg Gratings Within a Flexible Superstructure

2011· article· en· W1969849815 on OpenAlexaff
David A. Singlehurst, Christopher R. Dennison, Peter Wild

Bibliographic record

VenueJournal of Lightwave Technology · 2011
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsFiber Bragg gratingMultiplexingMaterials scienceCalibrationOpticsPressure sensorSuperstructureHydrostatic pressureFiber optic sensorPressure measurementOptical fiberSensitivity (control systems)PHOSFOSAcousticsOptoelectronicsElectronic engineeringStructural engineeringWavelengthEngineeringPlastic optical fiberMechanical engineeringPhysics

Abstract

fetched live from OpenAlex

In this work, a new distributed pressure measurement system is presented that is based on multiplexed in-fibre Bragg gratings housed within a flexible superstructure. The sensor superstructure comprises hypodermic tubing and several spacers and pressure diaphragms that together define the sensing locations and provide mechanical support to the Bragg gratings. Linear elasticity and strain-optic models are used to predict sensor performance in terms of sensitivity to hydrostatic pressure. Model predictions are validated through experimental calibration and indicate pressure sensitivities as high as 2.94 nm/MPa (MegaPascal) for a prototype with 1 mm outside diameter. The maximum measurement error from all calibration experiments is 3.4% of full-scale applied pressure. To the authors' knowledge, this is the smallest reported multiplexed pressure sensor that is based on Bragg gratings. Due to its small size, this sensor could potentially be applied in medical applications that existing sensors are too large for, specifically in angiography procedures for coronary arteries.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.022
GPT teacher head0.205
Teacher spread0.182 · 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.

Study designBench or experimental
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

Citations19
Published2011
Admission routes1
Has abstractyes

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