{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008927253,0.000856748,0.0008094095,0.000469657,0.0002321041,0.0005443863,0.001240915,0.0009787239,0.001927551],"category_scores_gemma":[0.003054045,0.0003246843,0.0005832315,0.00034965,0.0008030648,0.0007451313,0.001141363,0.001555567,0.000372283],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008110934,"about_ca_system_score_gemma":0.000588546,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002791365,"about_ca_topic_score_gemma":0.002474213,"domain_scores_codex":[0.9995029,0.0001537561,0.0000196638,0.0001334595,0.0001323712,0.00005787282],"domain_scores_gemma":[0.9984587,0.001071263,0.0001482411,0.0001006618,0.0001678771,0.00005329572],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001073135,0.00002262069,0.000732606,0.00004153933,0.00003431077,0.0001029279,0.00003249223,0.9327616,0.001846178,0.005742142,0.00104997,0.0575263],"study_design_scores_gemma":[0.000002491819,0.000008679374,0.00005521323,0.000002309654,0.000003466762,0.00002243466,0.000001786195,0.9976624,0.0004710279,0.001642415,0.0001251345,0.000002738519],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006257677,0.0001669874,0.992083,0.0001864773,0.00002641723,0.00002017688,0.00005164115,0.0003683922,0.0008393261],"genre_scores_gemma":[0.8492246,0.0002196486,0.1459594,0.0005021874,0.00009935761,0.0001157718,0.0002936393,0.00009251517,0.003492944],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002791365,"threshold_uncertainty_score":0.006448328,"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."}}