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Record W2809442059 · doi:10.1364/oe.26.017437

Simultaneous distributed static and dynamic sensing based on ultra-short fiber Bragg gratings

2018· article· en· W2809442059 on OpenAlexaff
Zhengying Li, Yuheng Tong, Xuelei Fu, Jiaqi Wang, Qingqing Guo, Haihu Yu, Xiaoyi Bao

Bibliographic record

VenueOptics Express · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsUniversity of Ottawa
FundersNatural Science Foundation of Hubei ProvinceNational Natural Science Foundation of China
KeywordsFiber Bragg gratingOpticsMaterials scienceDistributed acoustic sensingFiber optic sensorVibrationWavelengthOptical fiberReflection (computer programming)OptoelectronicsAcousticsPhysicsComputer science

Abstract

fetched live from OpenAlex

Distributed static and dynamic sensing is demonstrated with an ultra-short fiber Bragg grating (USFBG) array. The USFBGs serve as the sensors and reflection mirrors at the same time. Distributed static sensing is performed by demodulating the strain-induced or temperature-induced wavelength shift of each USFBG. Dynamic vibration sensing is realized based on phase variation between two adjacent USFBG reflected pulses. Static temperature and dynamic vibration are applied to the sensing ultra-short FBG array simultaneously. The experiments show that the temperature measurement from 30 °C to 80 °C is achieved successfully. And dynamic sensing of nε scale vibration and 12.5 kHz acoustic wave are demonstrated at a sampling rate of 50 kHz.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.174
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 head0.006
GPT teacher head0.227
Teacher spread0.221 · 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 designSimulation or modeling
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

Citations67
Published2018
Admission routes1
Has abstractyes

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