Active Control of Inflatable Structure Membrane Wrinkles Using Genetic Algorithm and Neural Network
Why this work is in the frame
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Bibliographic record
Abstract
This paper investigates the application of genetic algorithm and neural network in active control of inflatable structure membrane wrinkles. The membrane to be controlled is a 500mm × 500mm Kapton membrane, pulled by two pairs of forces applied at the four corners along the diagonals. Different combinations of the tensions produce various wrinkles within the membrane. The genetic algorithm is introduced briefly and then used in searching for the optimal force that minimizes the amplitude of the membrane. To predict the membrane flatness in space where direct measurement of membrane flatness is difficult, a neural network model is proposed to map boundary stretching tensions and space environment to membrane flatness. Numerical simulation shows that genetic algorithm works very well in optimizing the tensions and neural network is effective to estimate the flatness of the membrane.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it