{"id":"W2942341656","doi":"10.1002/mmce.21788","title":"Concurrent adaptive beamforming for standard hexagonal array based on dual norm‐constraint correntropy in the presence of alpha stable noise","year":2019,"lang":"en","type":"article","venue":"International Journal of RF and Microwave Computer-Aided Engineering","topic":"Advanced Adaptive Filtering Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"National Natural Science Foundation of China","keywords":"Norm (philosophy); Beamforming; Mathematical optimization; Adaptive beamformer; Mathematics; Constraint (computer-aided design); Algorithm; Noise power; Computer science; Convex optimization; Adaptive filter; Regular polygon; Control theory (sociology); Power (physics); Artificial intelligence; Telecommunications","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":[0.0003043831,0.0001919327,0.0002916424,0.0002599843,0.00001441886,0.00004221137,0.0003136103,0.00004523898,0.000005608059],"category_scores_gemma":[0.00004127169,0.0001622159,0.000106555,0.00007218185,0.00003532251,0.0002211493,0.00003686438,0.0002834156,4.78127e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001585272,"about_ca_system_score_gemma":0.00003933565,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001892434,"about_ca_topic_score_gemma":0.000001164678,"domain_scores_codex":[0.9988642,0.00001411437,0.0004784456,0.0001309682,0.0003099717,0.0002022406],"domain_scores_gemma":[0.9990326,0.0004148409,0.0001606825,0.0001025443,0.0002400016,0.00004938532],"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":[0.0001693134,0.0000506476,0.0002355532,0.0001067535,0.0001066182,0.00002391312,0.0004345229,0.7665074,0.2138694,0.002147732,0.0001125645,0.01623561],"study_design_scores_gemma":[0.002545269,0.001076107,0.0008288844,0.001906286,0.00002168328,0.0002318548,0.0001609642,0.8071333,0.1816571,0.0003239816,0.003759828,0.0003547718],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2280349,0.0001330211,0.770465,0.00004301502,0.0009637566,0.0002350727,0.00006845184,0.00002482191,0.00003197986],"genre_scores_gemma":[0.8940874,0.00004121149,0.1056254,0.00003140342,0.0001758275,0.00000870495,0.000006953231,0.00002104242,0.000002122358],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6660525,"threshold_uncertainty_score":0.6614974,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01030165463076002,"score_gpt":0.232048661281188,"score_spread":0.221747006650428,"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."}}