Bibliographic record
Abstract
In this paper, a machine learning method combining variational auto-encoder (VAE) and back propagation network (BPNN) is proposed to predict the performance of a double-T monopole antenna, which is the return loss <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(S_{11})$</tex> in frequency 1.5-6GHz. Then the trained machine learning model can take the place of the traditional electromagnetic simulation software CST, to predict <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$S_{11}$</tex> in a few seconds. Genetic algorithm (GA) is used to optimize <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$S_{11}$</tex> in frequency band 2.4-3.0GHz and 5.15-5.6GHz. This method shows an accurate and efficient prediction performance for that the average prediction errors of training and testing samples for all frequency points are 1.08% and 1.07%, respectively. The optimized antenna performance based on VAE-BPNN-GA is almost in accordance with that acquired by CST, that is, the average prediction error for all frequency points is 1.20%, while occupying less computation time. VAE-BPNN-GA is also compared with PCA-BPNN-GA, and proved to have higher prediction accuracy.
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How this classification was reachedexpand
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 | 0.001 | 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) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".