{"id":"W4399428625","doi":"10.3390/telecom5020023","title":"Enhancing Beamforming Efficiency Utilizing Taguchi Optimization and Neural Network Acceleration","year":2024,"lang":"en","type":"article","venue":"Telecom","topic":"Antenna Design and Optimization","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Université de Moncton","funders":"","keywords":"Taguchi methods; Artificial neural network; Beamforming; Computer science; Antenna array; Robustness (evolution); Radiation pattern; Antenna (radio); Orthogonal array; Electronic engineering; Artificial intelligence; Engineering; Machine learning; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000516428,0.0007867133,0.0003546589,0.0004768076,0.000155413,0.0005213537,0.0004999356,0.0005073458,0.001210346],"category_scores_gemma":[0.0009570532,0.0003103885,0.0003675689,0.0004978646,0.000278302,0.0006112875,0.0003728378,0.0004706728,0.0004770233],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004496246,"about_ca_system_score_gemma":0.0003318489,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009346576,"about_ca_topic_score_gemma":0.002118432,"domain_scores_codex":[0.999729,0.0000643417,0.00001578518,0.00004032091,0.000122117,0.00002841929],"domain_scores_gemma":[0.9996437,0.0001829403,0.00004919937,0.00003254604,0.00008343346,0.000008238632],"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":[0.00011286,0.00008146241,0.0006162288,0.0001398159,0.00005316265,0.00004729882,0.00006314011,0.6464675,0.09636685,0.006382388,0.0006157235,0.2490536],"study_design_scores_gemma":[0.00000459422,0.00004256026,0.000120362,0.000005276037,0.000007215237,0.00001414075,0.00000512556,0.9839697,0.01418029,0.0007998014,0.0008442975,0.000006534307],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01367295,0.0001235024,0.9833959,0.00004692371,0.00002501178,0.00001981287,0.00001055603,0.0002532483,0.002452052],"genre_scores_gemma":[0.3694838,0.0002754385,0.6264632,0.0000829291,0.0000271475,0.0001184296,0.00005532594,0.00009833415,0.003395458],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001210346,"threshold_uncertainty_score":0.004048944,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00991076896068035,"score_gpt":0.2115624596417818,"score_spread":0.2016516906811014,"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."}}