Knowledge-Based Conditional Generative Adversarial Network for Conformal Antenna Array Diagnosis
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Bibliographic record
Abstract
In this letter, we propose a novel machine learning (ML)-based method for real-time diagnosis of impaired conformal antenna arrays. An improved conditional generative adversarial network (cGAN) is first applied to the array diagnosis. Specifically, a generator is used to generate the diagnosed excitations, and a discriminator is used to determine whether the diagnosed excitations are real. The impaired far-field pattern is sampled to be fed as conditions into the discriminator and generator. Additionally, as prior knowledge, the sparsity of the impaired pattern is used to improve the proposed network and to enhance diagnostic accuracy. Examples of diagnosis and comparisons with existing ML-based techniques demonstrate that the proposed approach has a higher diagnostic accuracy, even with a smaller number of measurements
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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