An Ultra Wideband Crossover Fed by Printed Gap Waveguide Using Machine-Learning-Assisted Optimization
Bibliographic record
Abstract
In this paper, an ultra-wideband planar crossover using printed gap waveguide (PGW) technology is proposed and designed for millimetre-wave applications. A machine learning (ML) technique is used to optimize the proposed crossover performance. The ML-based surrogate model has been trained and tested for different ML algorithms. The k-nearest neighbor (KNN) algorithm is used to predict potential designs during optimization. The simulated result of the designed crossover offers an ultra-operating frequency bandwidth from 26 to 40 GHz$\boldsymbol{(} \mathbf{4 2. 4 2 \%} \boldsymbol{)}$, where the return loss level is better than$\mathbf{1 2 ~ d B}$and the isolation level is less than$\mathbf{1 1 ~ d B}$.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".