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Record W2793052746 · doi:10.1109/jlt.2018.2800694

Self-Referenced Broad-Range Optical Rotation Sensor for Flight Control Applications

2018· article· en· W2793052746 on OpenAlexafffund
Tahereh Ahmadi Tameh, Raman Kashyap, Mohamad Sawan

Bibliographic record

VenueJournal of Lightwave Technology · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvanced Measurement and Metrology Techniques
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsConsortium de Recherche et d’innovation en Aérospatiale au QuébecCMC Microsystems
KeywordsRotary encoderAvionicsRange (aeronautics)Rotation (mathematics)Electronic engineeringEncoderMaterials scienceEngineeringOpticsComputer sciencePhysicsAerospace engineering

Abstract

fetched live from OpenAlex

In this paper, we present a self-referenced broad range optical rotary sensor (ORS) for avionics applications. To achieve the high reliability required for aerospace use, the rotary encoder operates ratiometrically to make it insensitive to source light power variation. This sensor has a higher operating range than rotary variable differential transformers (RVDTs) currently used in fly-by-wire aircraft. The ORS encoder is fabricated on an antireflection coated glass substrate with a reflective aluminum coating. Experimental results prove that the sensor accuracy is 0.8% over the full rotation range from 0° to 356.5°. The proposed ORS has better accuracy and an operating range of at least twice that of RVDTs, which have an accuracy of 1% over a full range of only 80°. We also test the insensitivity of the sensor to source fluctuations, which confirms that the sensor is self-referencing.

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: Methods · Consensus signal: none
Teacher disagreement score0.776
Threshold uncertainty score0.510

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 head0.012
GPT teacher head0.257
Teacher spread0.245 · 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
GenreMethods

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

Citations7
Published2018
Admission routes2
Has abstractyes

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