MétaCan
Menu
Back to cohort
Record W2046086433 · doi:10.1117/12.758063

An electromagnetically actuated fiber optic switch using magnetized ferromagnetic materials

2007· article· en· W2046086433 on OpenAlexaff
Praveen Pandojirao-S, Naresh Dhaubanjar, Pratibha C. Phuyal, Mu Chiao, J.‐C. Chiao

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2007
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMaterials scienceFerromagnetismOptical fiberFiberOptical switchFabricationHysteresisOptoelectronicsOpticsComposite materialCondensed matter physicsPhysics

Abstract

fetched live from OpenAlex

This paper presents the design, fabrication and testing of a fiber optic switch actuated electromagnetically. The ferromagnetic gel coated optical fiber is actuated using external electromagnetic fields. The ferromagnetic gel consists of ferromagnetic powders dispersed in epoxy. The fabrication utilizes a simple cost-effective coating setup. A direct fiberto-fiber alignment eliminates the need for complementary optical parts and the displacement of fiber switches the laser coupling. The magnetic characteristics of magnetized ferromagnetic materials are performed using alternating gradient magnetometer and the magnetic hysteresis curves are measured for different ferromagnetic materials including iron, cobalt, and nickel. Optical fiber switches with various fiber lengths are actuated and their static and dynamic responses for the same volume of ferromagnetic gel are summarized. The highest displacement is 1.345 mm with an input current of 260mA. In this paper, the performance of fiber switches with various coating materials is presented.

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)0.000
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
Metaresearch0.0000.000
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)0.0010.000

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 head0.010
GPT teacher head0.237
Teacher spread0.226 · 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

Citations2
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicAdvanced Fiber Optic SensorsFrench-language works237,207