MétaCan
Menu
Back to cohort
Record W2468171748 · doi:10.1109/jsen.2016.2588981

Novel Analog Ratio-Metric Optical Rotary Encoder for Avionic Applications

2016· article· en· W2468171748 on OpenAlexafffund
Tahereh Ahmadi Tameh, Mohamad Sawan, Raman Kashyap

Bibliographic record

VenueIEEE Sensors Journal · 2016
Typearticle
Languageen
FieldComputer Science
TopicSensor Technology and Measurement Systems
Canadian institutionsPolytechnique Montréal
FundersFonds de recherche du Québec – Nature et technologiesThales GroupCanada Research ChairsGovernment of CanadaConsortium de Recherche et d’innovation en Aérospatiale au QuébecNatural Sciences and Engineering Research Council of CanadaCMC MicrosystemsMitacsBombardier
KeywordsAvionicsEncoderComputer scienceMetric (unit)Electronic engineeringEngineeringAerospace engineering

Abstract

fetched live from OpenAlex

In this paper, an optical, analog, self-referencing, ratio-metric, smart displacement sensor is proposed for avionic applications. The position of rotation is determined by the ratio of the transmitted and reflected light powers, which makes the sensor independent of power fluctuations. A single multi-gradient encoder design compensates for the use of a non-uniform source. An anti-reflection coated glass window with the outer diameter of 27 mm is used with an encoder pattern mapped on it by aluminum deposition. The experimental results show that the ratio of the transmitted and reflected powers has an accuracy of 0.53% over the full range, matching the specifications for avionic applications. It is also experimentally proved that the sensor operates ratio-metrically and not affected by the change in the source power, which makes it highly reliable for the avionic application. Further optimization of the design will make this type of sensor an excellent choice for future lightweight and greener aircraft technology.

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)0.001
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: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.036
GPT teacher head0.270
Teacher spread0.234 · 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

Citations42
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueIEEE Sensors JournalSame topicSensor Technology and Measurement SystemsFrench-language works237,207