MétaCan
Menu
Back to cohort
Record W2102788166 · doi:10.1109/cca.2005.1507188

Development of GA-based control system for active shape control of inflatable space structures

2005· article· en· W2102788166 on OpenAlexaff
Fujun Peng, Yan‐Ru Hu, Alfred Ng

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicStructural Analysis and Optimization
Canadian institutionsCanadian Space AgencyNatural Sciences and Engineering Research Council of Canada
Fundersnot available
KeywordsInflatableActuatorControl systemShape-memory alloyDevelopment (topology)KaptonControl (management)Genetic algorithmSpace (punctuation)Computer scienceEngineeringControl theory (sociology)Materials scienceMechanical engineeringArtificial intelligenceNanotechnologyMathematicsElectrical engineering

Abstract

fetched live from OpenAlex

This paper describes the development of a control system used for the active shape control of inflatable space structures. The genetic algorithm is utilized for the optimization of control variables. A vision system is implemented for the measurement of the structure shape. Shape memory alloy wire actuators are used to exert the obtained optimal tensions. The developed control system is then tested on a 200mm times 300mm rectangular Kapton membrane structure. The membrane is pulled by three tensions along each edge. Different combinations of the tensions produce various wrinkles on the membrane. Test results indicate that the developed control system works very well in improving the structure shape precision

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

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

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 head0.005
GPT teacher head0.192
Teacher spread0.187 · 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 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

Citations8
Published2005
Admission routes1
Has abstractyes

Explore more

Same topicStructural Analysis and OptimizationFrench-language works237,207