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Record W4405022215 · doi:10.1109/jsen.2024.3506744

Compensation of Scanning Speed Instability in a Wavelength-Swept Laser for Dynamic Fiber Optic Sensors

2024· article· en· W4405022215 on OpenAlexfundno aff
Byeong Kwon Choi, Sung Yoon Cho, Soyeon Ahn, Ji Su Kim, Jae Ok Yoo, Min Yong Jeon

Bibliographic record

VenueIEEE Sensors Journal · 2024
Typearticle
Languageen
FieldEngineering
TopicSemiconductor Lasers and Optical Devices
Canadian institutionsnot available
FundersNational Research Foundation of KoreaKorea Institute for Advancement of TechnologyOntario Ministry of Research, Innovation and Science
KeywordsCompensation (psychology)Fiber laserOpticsOptical fiberMaterials scienceWavelengthInstabilityFiber optic sensorLaserFiberOptoelectronicsPhysics

Abstract

fetched live from OpenAlex

The scanning speed stability of a wavelength-swept laser (WSL) with a polygon-scanner-based wavelength filter (PSWF) is affected by the speed of the brushless direct current (BLDC) motor driving a polygonal scanner mirror (PSM). In particular, the PSM running at low speeds experiences fluctuations in rotational speed, causing the output of the WSL to fluctuate in the temporal domain. The speed variation of the WSL causes critical measurement errors in the fiber Bragg grating (FBG) interrogator sensors. In this study, we successfully corrected the output error of the FBG array sensor caused by the PSM rotational speed fluctuations in a WSL running at a low scanning rate. The FBG array used five FBGs, and the time intervals of the FBGs were corrected according to the PSM rotation speed variation based on the periodic interval of the array signal. A WSL with a scanning rate of 1.0 kHz suggested a 97% improvement after compensating for rotational speed variations of the PSM.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.099
Threshold uncertainty score0.667

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 head0.017
GPT teacher head0.260
Teacher spread0.243 · 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 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

Citations2
Published2024
Admission routes1
Has abstractyes

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