{"id":"W4392024252","doi":"10.1109/lawp.2024.3368475","title":"Knowledge-Based Conditional Generative Adversarial Network for Conformal Antenna Array Diagnosis","year":2024,"lang":"en","type":"article","venue":"IEEE Antennas and Wireless Propagation Letters","topic":"Integrated Circuits and Semiconductor Failure Analysis","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Science Foundation of Sichuan Province; National Natural Science Foundation of China","keywords":"Discriminator; Generator (circuit theory); Computer science; Conformal map; Generative grammar; Antenna (radio); Generative adversarial network; Artificial intelligence; Adversarial system; Pattern recognition (psychology); Conformal antenna; Machine learning; Deep learning; Mathematics; Radiation pattern; Telecommunications; Physics; Power (physics)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001471113,0.0002418109,0.0002570839,0.0001411244,0.0001827618,0.0002088989,0.00007534594,0.0001125121,0.00006157425],"category_scores_gemma":[0.000009124013,0.0002113698,0.0001573873,0.0002857616,0.00009893048,0.0003185,0.000003669284,0.0002004538,0.00002303688],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006751347,"about_ca_system_score_gemma":0.00007220056,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001038879,"about_ca_topic_score_gemma":0.00003509809,"domain_scores_codex":[0.9989064,0.00003253014,0.0003099352,0.0002931102,0.0001288632,0.000329113],"domain_scores_gemma":[0.9994665,0.0001476597,0.00003593427,0.0001054209,0.0001631275,0.00008130296],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005052471,0.00004184244,0.0002589992,0.0004504389,0.0007739367,0.0000431505,0.0008014224,0.02448993,0.8932498,0.008484186,0.06367873,0.007677064],"study_design_scores_gemma":[0.0008051465,0.00007191325,0.00009153417,0.0002707479,0.0002416579,0.00001563068,0.0001318272,0.8571658,0.1160701,0.0004343778,0.02414969,0.0005515763],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2374051,0.001230496,0.755236,0.002139826,0.002445251,0.0004757461,0.0003522685,0.0004156869,0.0002996204],"genre_scores_gemma":[0.9963361,0.0001665064,0.0004284051,0.001028636,0.00122783,0.0002264456,0.0004607384,0.0000451691,0.00008018137],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8326758,"threshold_uncertainty_score":0.8619409,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01327469822672525,"score_gpt":0.2279184257083946,"score_spread":0.2146437274816693,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}