{"id":"W4360592266","doi":"10.1007/978-981-99-0631-4_47","title":"Multi-objective Optimization of IPT System Compensation Parameters for Improving Misalignment Tolerance","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in electrical engineering","topic":"Wireless Power Transfer Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Ripple; Control theory (sociology); Particle swarm optimization; Compensation (psychology); Set (abstract data type); Pareto principle; Voltage; Current (fluid); Power (physics); Engineering; Computer science; Mathematical optimization; Mathematics; Electrical engineering; Control (management)","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001999273,0.0005521822,0.0008525114,0.000557459,0.00002774704,0.00002926516,0.0002201644,0.0006148826,0.000001840699],"category_scores_gemma":[0.000128088,0.0006252802,0.000211118,0.0002585666,0.00001739136,0.00007039659,0.0000165345,0.0005802205,0.000003505359],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008985432,"about_ca_system_score_gemma":0.00003588069,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003156385,"about_ca_topic_score_gemma":0.0000210541,"domain_scores_codex":[0.9979289,0.00001407864,0.0007950121,0.0004571024,0.000317368,0.0004875037],"domain_scores_gemma":[0.9987754,0.0006431662,0.0001126146,0.0002985903,0.00009353719,0.00007670748],"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":[0.00001925765,0.000007722877,0.000004289932,0.001969516,0.000109445,0.000005426325,0.0002038178,0.989234,0.00448858,0.0009368582,0.000002367817,0.00301875],"study_design_scores_gemma":[0.0006185398,0.00008209434,0.00001374469,0.0008728824,0.00006939354,0.000005419367,0.000002470858,0.9877599,0.00997079,0.00003298362,0.00003818576,0.0005335995],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0002304903,0.0007854538,0.9954605,0.000007218946,0.001068025,0.001341982,0.00005704125,0.0007599858,0.0002892988],"genre_scores_gemma":[0.9537774,0.00005041272,0.04506169,0.000006558888,0.0001884176,0.000245889,0.00010573,0.0004129837,0.0001509139],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9535469,"threshold_uncertainty_score":0.9996198,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01102557112085006,"score_gpt":0.1992504866890083,"score_spread":0.1882249155681582,"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."}}