{"id":"W4400312677","doi":"10.1016/j.compeleceng.2024.109441","title":"Multi-objective optimization model of Ultra-High Voltage Direct Current system considering low carbon and equipment safety based on Im-NSGA-II and ResNet-LSTM","year":2024,"lang":"en","type":"article","venue":"Computers & Electrical Engineering","topic":"Power System Reliability and Maintenance","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"National Natural Science Foundation of China","keywords":"Current (fluid); Low voltage; Voltage; Computer science; Multi-objective optimization; Reliability engineering; Engineering; Materials science; Electrical engineering; Machine learning","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":[0.0002563513,0.0003040963,0.0004438532,0.0002567507,0.00005507046,0.0000487848,0.00009392803,0.000101081,4.005623e-7],"category_scores_gemma":[0.00005871191,0.0003014013,0.00006476183,0.000323675,0.00002922669,0.00008892514,0.00004359864,0.0003066799,3.150772e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003850548,"about_ca_system_score_gemma":0.00003561608,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001670469,"about_ca_topic_score_gemma":7.812878e-7,"domain_scores_codex":[0.9985806,0.00002732767,0.0004071619,0.0004152065,0.000192739,0.0003769332],"domain_scores_gemma":[0.9992937,0.0003068848,0.00003289553,0.000203272,0.00004254575,0.0001207433],"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":[0.00001886766,0.00002393969,0.000008754154,0.001671991,0.00004960833,0.000008825559,0.0002293351,0.9893575,0.005785391,0.0004066449,0.0000120362,0.002427079],"study_design_scores_gemma":[0.0004372087,0.00009901376,0.00005337213,0.001675748,0.00003111513,0.000009081916,0.000005784552,0.993651,0.003682319,0.000003400272,0.00006394944,0.0002879763],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02149193,0.003805272,0.9727142,0.0000126731,0.0008039235,0.0003982992,0.00001880762,0.0006518513,0.0001029882],"genre_scores_gemma":[0.9908379,0.0001784147,0.008840091,0.000006315875,0.00004237051,0.00003161544,0.000006577088,0.00005212385,0.000004649522],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9693459,"threshold_uncertainty_score":0.9999438,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006448735807789838,"score_gpt":0.1898610487549326,"score_spread":0.1834123129471428,"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."}}