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Record W2897004675 · doi:10.1109/jlt.2018.2876009

High-Speed and High-Resolution Interrogation of a Strain and Temperature Random Grating Sensor

2018· article· en· W2897004675 on OpenAlexafffund
Hong Deng, Ping Lü, Stephen J. Mihailov, Jianping Yao

Bibliographic record

VenueJournal of Lightwave Technology · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsNational Research Council CanadaUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsOpticsFiber Bragg gratingMaterials scienceGratingBirefringencePulse compressionOptical fiberWaveformWavelengthFiber optic sensorInterrogationDiffraction gratingOptoelectronicsPhysicsVoltageTelecommunicationsComputer science

Abstract

fetched live from OpenAlex

High-speed and high-resolution interrogation of a random fiber grating sensor based on spectral shaping and wavelength-to-time (SS-WTT) mapping, and pulse compression for simultaneous measurement of strain and temperature is proposed and demonstrated. In the proposed system, an ultrashort pulse is spectrum shaped by a high-birefringence (Hi-Bi) random grating (HBRG) to generate two orthogonally polarized spectra with a wavelength difference determined by the birefringence of the HBRG, which are then fed to a dispersive optical loop in which a linearly chirped fiber Bragg grating (LCFBG) is incorporated, to perform linear WTT mapping, to generate two temporally separated optical pulses, which are converted to two random electrical waveforms at a photodetector). Random pulse compression is then performed to increase the interrogation resolution. By measuring the time shifts of the temporally compressed pulses, the strain and temperature information is retrieved. An experiment is performed. The experimental results show that the proposed random grating sensor and its interrogation system can provide a strain and temperature resolution of 7.1 μϵ and 0.79 °C at an ultrahigh speed of 20 MHz.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.440

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 head0.006
GPT teacher head0.215
Teacher spread0.210 · 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 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

Citations17
Published2018
Admission routes2
Has abstractyes

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