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Record W2797484651 · doi:10.3390/s18041207

Continuous-Wave Fiber Cavity Ringdown Pressure Sensing Based on Frequency-Shifted Interferometry

2018· article· en· W2797484651 on OpenAlexaff
Yiwen Ou, Chunfu Cheng, Zehao Chen, Zhangyong Yang, Hui Lv, Li Qian

Bibliographic record

VenueSensors · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsUniversity of Toronto
FundersHubei UniversityHubei University of TechnologyNational Natural Science Foundation of China
KeywordsInterferometrySensitivity (control systems)OpticsMaterials sciencePressure sensorFiber optic sensorOptical fiberPressure measurementFiberContinuous wavePhysicsElectronic engineeringLaserEngineering

Abstract

fetched live from OpenAlex

We present a continuous-wave fiber cavity ringdown (FCRD) pressure-sensing method based on frequency-shifted interferometry (FSI). Compared with traditional CRD or FCRD techniques, this FSI-FCRD scheme deduces pressure by measuring the decay rate of continuous light exiting the fiber ringdown cavity (RDC) in the spatial domain (i.e., the CRD distance), without the requirement for optical pulsation and fast electronics. By using a section of fiber with the buffer layer stripped in the fiber RDC as the sensor head, pressures were measured within the range from 0 to 10.4 MPa. The sensitivity of 0.02356/(km∙MPa) was obtained with a measurement error of 0.1%, and the corresponding pressure resolution was 0.05 MPa. It was found that the measurement sensitivity can be improved by enlarging the interaction length of the sensor head. The results show the proposed sensor has the advantages of simple structure, low cost, high sensitivity, and high stability in pressure 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: Empirical · Consensus signal: Empirical
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)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.013
GPT teacher head0.224
Teacher spread0.211 · 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
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

Citations16
Published2018
Admission routes1
Has abstractyes

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