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Record W2221471780 · doi:10.1016/j.ifacol.2015.08.052

Line of sight stabilization controllers tuning from high-level Modulation Transfer Function specifications

2015· article· en· W2221471780 on OpenAlexaff
Sophie Frasnedo, Cédric Chapuis, Gilles Duc, Ph. Feyel, Guillaume Sandou

Bibliographic record

VenueIFAC-PapersOnLine · 2015
Typearticle
Languageen
FieldEngineering
TopicAdvanced Control and Stabilization in Aerospace Systems
Canadian institutionsSafran Electronics (Canada)
Fundersnot available
KeywordsOptical transfer functionInertial frame of referenceImage stabilizationComputer scienceControl theory (sociology)Transfer functionLine-of-sightController (irrigation)Frame (networking)Motion blurImage qualityMotion (physics)Inertial measurement unitSightComputer visionModulation (music)Motion controllerMotion controlImage (mathematics)Artificial intelligenceEngineeringControl (management)OpticsPhysicsAcousticsAerospace engineeringTelecommunications

Abstract

fetched live from OpenAlex

A method to tune the parameters of the controller of an inertially stabilized platform is presented. This platform carries an electro-optical system. The image quality is obviously influenced by the movements of the platform: the Line of Sight (LoS) of the imager has to remain fixed in an inertial frame. The more the LoS controller manages to counter the movement of the platform, the better the image quality will be. The motion Modulation Transfer Function (motion MTF) measures the amount of blur brought into the image by the motion of the platform. It represents the contrast over spatial frequencies. Up to now, it has mostly been used as a validation tool for controllers already tuned from derived low level and conservative considerations. The proposed methodology aims to tune LoS controllers using directly the motion MTF as a criterion in the design procedure.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.795
Threshold uncertainty score0.900

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 head0.067
GPT teacher head0.225
Teacher spread0.159 · 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

Citations1
Published2015
Admission routes1
Has abstractyes

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