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Record W1968528584 · doi:10.1117/1.oe.52.6.065003

Automated alignment of microstructured optical fibers and conventional single-mode fibers

2013· article· en· W1968528584 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueOptical Engineering · 2013
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsUniversité de Moncton
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsOptical fiberSingle-mode optical fiberParticle swarm optimizationComputer scienceOpticsMaterials sciencePower (physics)Optical powerFiberOptical engineeringCoupling (piping)Position (finance)AlgorithmTelecommunicationsPhysicsLaser

Abstract

fetched live from OpenAlex

Aligning two optical fibers is a crucial step in designing good optical fiber–based systems and networks. Good alignment optimizes the power transmitted between the fibers, especially when a microstructured optical fiber (MOF) is interfaced with a single-mode fiber (SMF). In this paper, we present a self-alignment algorithm based on particle swarm optimization (PSO). The PSO algorithm is used to locate the optimal coupling position with the highest optical power for alignments with multiple degrees of freedom. The proposed algorithm is validated by applying it for two different sources and checking the achieved alignment of SMF and MOF and that of two SMFs.

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.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.783
Threshold uncertainty score0.839

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 head0.005
GPT teacher head0.196
Teacher spread0.191 · 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