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Record W3159292895 · doi:10.1364/ao.426462

Co-located angularly offset fiber Bragg grating pair for temperature-compensated unambiguous 3D shape sensing

2021· article· en· W3159292895 on OpenAlexafffund
Dingyi Feng, Jacques Albert, Yueguo Hou, Biqiang Jiang, Yajun Jiang, Ying Ma, Jianlin Zhao

Bibliographic record

VenueApplied Optics · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsOpticsFiber Bragg gratingMaterials scienceOffset (computer science)PHOSFOSFiber optic sensorOptical fiberPolarization-maintaining optical fiberPhysics

Abstract

fetched live from OpenAlex

A 10 mm-long three-dimensional shape sensor in a single-mode fiber is described and demonstrated experimentally. The sensor is based on a pair of fiber Bragg gratings inscribed at the same location along the fiber axis but offset along different radial directions away from the fiber center. Each offset grating generates cladding mode resonances over a ${\sim}{20}\;{\rm{nm}}$-wide spectral bandwidth, and the two gratings are also offset in period so that their transmission spectra are separated by 40 nm, and thus non-overlapping and fully distinguishable. Directional bending sensitivity results from the differential amplitude response of the cladding mode resonances from the two gratings, depending on the relative orientation of the bend with the azimuthal direction of the grating offsets. It is further demonstrated that both axial deformation and temperature have no influence on the shape measurement as they both only cause a global wavelength shift of the spectra without amplitude change. The experimental results demonstrate that the shape orientation of an object can be unambiguously determined for bend directions covering the full 360° range around the fiber axis with sensitivities of the order of ${{1}}\;{\rm{dB/}}{{\rm{m}}^{- 1}}$ and small curvatures between 0 and ${{1}}\;{{\rm{m}}^{- 1}}$.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.381
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.011
GPT teacher head0.227
Teacher spread0.215 · 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

Citations6
Published2021
Admission routes2
Has abstractyes

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