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Record W4392370873 · doi:10.18280/jesa.570119

Design and Control of a Laser Cooling System for Wavelength Stabilization of a Monochromatic Light Source

2024· article· en· W4392370873 on OpenAlexvenueno aff
Sulaf Waiss, Ayad Dalloo

Bibliographic record

VenueJournal Européen des Systèmes Automatisés · 2024
Typearticle
Languageen
FieldEngineering
TopicSemiconductor Lasers and Optical Devices
Canadian institutionsnot available
Fundersnot available
KeywordsMonochromatic colorWavelengthOpticsLaserLight sourceControl (management)Materials scienceOptoelectronicsPhysicsComputer science

Abstract

fetched live from OpenAlex

Most laser diodes experience fluctuations in intensity, phase, and wavelength, predominantly due to heating from energy conversion to thermal energy and changing ambient temperatures.This study primarily focuses on countering the effects of temperature variations through the design and implementation of a laser diode temperature controller (LDTC) for the fiber optical interferometer system.Additionally, there are other contributing factors, including the injected current, noises, and acoustic disturbances, that play significant roles in these fluctuations.Despite these additional factors, our findings emphasize that temperature remains the principal contributor to these fluctuations.The LDTC developed in this work is cost-effective and rapidly responsive to temperature changes, ensuring precise control over the laser diode's temperature.The findings show the ability of this controller to maintain the temperature of a laser diode at a constant value with a precision (steady-state error) of 0.0013℃ (0.0396℃ without the laser diode) and an average fluctuation of 0.0567℃ (0.0742℃ without the laser diode).Furthermore, the study establishes a relationship between the length of the measurement object and temperature.This laser diode controller was investigated to measure the wavelengthdependent temperature changes at the interferometric point sensor.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.659
Threshold uncertainty score0.554

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.014
GPT teacher head0.223
Teacher spread0.210 · 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 designSimulation or modeling
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
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueJournal Européen des Systèmes AutomatisésSame topicSemiconductor Lasers and Optical DevicesFrench-language works237,207