MétaCan
Menu
Back to cohort
Record W2894702751 · doi:10.3390/app8101820

Recent Progress in Fast Distributed Brillouin Optical Fiber Sensing

2018· article· en· W2894702751 on OpenAlexaff
Hongying Zhang, Dengwang Zhou, Benzhang Wang, Chao Pang, Pengbai Xu, Taofei Jiang, Dexin Ba, Hui Li, Yongkang Dong

Bibliographic record

VenueApplied Sciences · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsUniversity of Ottawa
FundersNational Key Scientific Instrument and Equipment Development Projects of ChinaNatural Science Foundation of Heilongjiang ProvinceChina Scholarship CouncilNational Natural Science Foundation of China
KeywordsDistributed acoustic sensingOptical fiberBrillouin scatteringBrillouin zoneComputer scienceChirpFiber optic sensorMaterials scienceOpticsElectronic engineeringTelecommunicationsEngineeringPhysicsLaser

Abstract

fetched live from OpenAlex

Brillouin-based optical fiber sensing has been regarded as a good distributed measurement tool for the modern large geometrical structure and the industrial facilities because it can demodulate the distributed environment information (e.g., temperature and strain) along the sensing fiber. Brillouin optical time domain analysis (BOTDA), which is an excellent and attractive scheme, has been widely developed thanks to its high performance in a signal-to-noise ratio, a spatial resolution, and sensing distance. However, the sampling rate of the classical BOTDA is severely limited by several factors (especially the serially frequency-sweeping process) so that it cannot be suitable for the quickly distributed measurement. In this work, we summarize some promising breakthroughs about the fast BOTDA, which can be named as an optical frequency comb technique, an optical frequency-agile technique, a slope-assisted technique, and an optical chirp chain technique.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.953
Threshold uncertainty score0.492

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.015
GPT teacher head0.254
Teacher spread0.239 · 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 designOther design
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

Citations55
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueApplied SciencesSame topicAdvanced Fiber Optic SensorsFrench-language works237,207