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Record W1966430475 · doi:10.1117/12.467253

Optical implementation of closed loop controllers for mechatronic systems

2002· article· en· W1966430475 on OpenAlexafffund
D. Necsulescu, Anurag Bhagia

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2002
Typearticle
Languageen
FieldComputer Science
TopicSensor Technology and Measurement Systems
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsController (irrigation)SIGNAL (programming language)ServomotorComputer sciencePhotodiodeElectronic engineeringVoltageDC motorTransmission (telecommunications)Electrical engineeringEngineeringTelecommunicationsPhysics

Abstract

fetched live from OpenAlex

The paper investigates the implementation of closed loop controllers of optomechatronic systems using optical components for signal transmission and control loop implementation. The system used for illustration is a DC position servomotor under state feedback control. The comparison is carried out between electric-electronic implementation and optical implementation for an environment with significant electromagnetic noise. For the optical implementation a photodiode is used for converting analog voltage output from position and velocity sensors into modulated 900 nm light, transmitted over a significant distance by a multi mode glass fiber (MMGF) to a controller implemented with a tunable optical amplifier. As an alternative, attenuators can also be used for controller implementation. The signal from controller is again transmitted 1550 nm MMGF to a photodetector that converts the signal into an analog voltage input to motor driver. Experimental results will illustrate the performance of the proposed optical implementation and advantages vs. electric- electronic implementation using a Lab View platform for data acquisition and display.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.607
Threshold uncertainty score0.954

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.020
GPT teacher head0.240
Teacher spread0.221 · 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
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

Citations0
Published2002
Admission routes2
Has abstractyes

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Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicSensor Technology and Measurement SystemsFrench-language works237,207