{"id":"W4285103619","doi":"10.1109/isgt50606.2022.9817514","title":"Robust Autoencoder-based State Estimation in Power Systems","year":2022,"lang":"en","type":"article","venue":"","topic":"Smart Grid Security and Resilience","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Autoencoder; Computer science; Estimator; Reliability (semiconductor); State (computer science); Exploit; Process (computing); Smart grid; Measure (data warehouse); Data mining; Artificial intelligence; Units of measurement; Task (project management); Construct (python library); Cyber-physical system; Power (physics); Algorithm; Deep learning; Computer security; Statistics; Mathematics; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0009303228,0.0006004525,0.0007780241,0.0003049905,0.0001974804,0.0005294245,0.000581198,0.0007360847,0.000724486],"category_scores_gemma":[0.002773079,0.0005069519,0.0005183941,0.0003766634,0.0006905701,0.0006588855,0.0005554466,0.001109218,0.0002035446],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005769053,"about_ca_system_score_gemma":0.000660187,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01056648,"about_ca_topic_score_gemma":0.005405241,"domain_scores_codex":[0.999666,0.00009647196,0.00002244691,0.00009807775,0.00008615119,0.00003089098],"domain_scores_gemma":[0.999003,0.0006876573,0.0001025834,0.00006853923,0.0001221726,0.00001611966],"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":[0.00001805366,0.000008947844,0.0001734217,0.00002638998,0.00002215361,0.00001623381,0.0000192334,0.9761273,0.001064884,0.002224471,0.0001642007,0.02013465],"study_design_scores_gemma":[6.417029e-7,0.000003387719,0.00004255898,0.000001663571,0.000001365606,0.000002209628,5.770968e-7,0.9992312,0.0001854396,0.0004774519,0.00005211612,0.000001430648],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01421036,0.0004196731,0.9842138,0.0001054282,0.00002652039,0.00001114848,0.00002515228,0.0003155963,0.0006723795],"genre_scores_gemma":[0.8735447,0.0006400713,0.1224822,0.0001185099,0.0000784263,0.00007118745,0.0001752099,0.0001027436,0.002786939],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01056648,"threshold_uncertainty_score":0.02100992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01110710504425509,"score_gpt":0.1913042755930706,"score_spread":0.1801971705488155,"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."}}