{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001203071,0.000113616,0.0000928414,0.00007680967,0.0001179547,0.0001775695,0.00004527915,0.00005776114,0.00005046475],"category_scores_gemma":[0.0000104208,0.0001147152,0.00002260575,0.0002771688,0.00001235871,0.0003622517,0.0000147719,0.0001191586,0.000006911107],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003551603,"about_ca_system_score_gemma":0.000009573466,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003263029,"about_ca_topic_score_gemma":0.000006040599,"domain_scores_codex":[0.9993798,0.00001148115,0.0001767393,0.0001463805,0.00007547044,0.0002101532],"domain_scores_gemma":[0.999821,0.00004210717,0.00001271674,0.00006884003,0.00001620975,0.00003914196],"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":[0.000001459305,0.000002073728,0.00005213213,0.00005165867,0.000006868334,0.000002440638,0.0002477163,0.989816,0.002547282,0.0003745485,0.0001341166,0.006763736],"study_design_scores_gemma":[0.00006088916,0.00001625342,0.00005269633,0.00006664314,0.00001200018,0.00001124794,0.00004548829,0.9982644,0.001119711,0.000046817,0.0001760591,0.0001277853],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04069165,0.002306334,0.9545193,0.00002931381,0.0005130772,0.000128709,7.907485e-7,0.0007129119,0.001097918],"genre_scores_gemma":[0.9731663,0.0002698502,0.02620105,0.00003898186,0.0001902353,0.000007812556,0.0000237002,0.00003502435,0.00006701864],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9324747,"threshold_uncertainty_score":0.467795,"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."}}