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$(S_{11})$in frequency 1.5-6GHz. Then the trained machine learning model can take the place of the traditional electromagnetic simulation software CST, to predict$S_{11}$in a few seconds. Genetic algorithm (GA) is used to optimize$S_{11}$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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".