MétaCan
Menu
Back to cohort
Record W2428250579 · doi:10.1007/s13320-016-0334-8

Study of Ф-OTDR stability for dynamic strain measurement in piezoelectric vibration

2016· article· en· W2428250579 on OpenAlexafffund
Meiqi Ren, Ping Lü, Liang Chen, Xiaoyi Bao

Bibliographic record

VenuePhotonic Sensors · 2016
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsReflectometryMaterials scienceLead zirconate titanateOptical time-domain reflectometerOpticsPiezoelectricityVibrationAcousticsHarmonicsRayleigh scatteringPhysicsFiber optic sensorTime domainVoltageOptical fiberDielectricOptoelectronicsComputer sciencePolarization-maintaining optical fiber

Abstract

fetched live from OpenAlex

Abstract In a phase-sensitive optical-time domain reflectometry (Φ-OTDR) system, the challenge for dynamic strain measurement lies in large intensity fluctuations from trace to trace. The intensity fluctuation caused by stochastic characteristics of Rayleigh backscattering sets detection limit for the minimum strength of vibration measurement and causes the large measurement uncertainty. Thus, a trace-to-trace correlation coefficient is introduced to quantify intensity fluctuation of Φ-OTDR traces and stability of the sensor system theoretically and experimentally. A novel approach of measuring dynamic strain induced by various driving voltages of lead zirconate titanate (PZT) in Φ-OTDR is also demonstrated. Piezoelectric vibration signals are evaluated through analyzing peak values of fast Fourier transform spectra at the fundamental frequency and high-order harmonics based on Bessel functions. High trace-to-trace correlation coefficients varying from 0.824 to 0.967 among 100 measurements are obtained in experimental results, showing the good stability of our sensor system, as well as small uncertainty of measured peak values.

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 score0.419
Threshold uncertainty score0.631

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 head0.030
GPT teacher head0.251
Teacher spread0.220 · 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

Citations19
Published2016
Admission routes2
Has abstractyes

Explore more

Same venuePhotonic SensorsSame topicAdvanced Fiber Optic SensorsFrench-language works237,207