{"id":"W4404564826","doi":"10.1109/eeeic/icpseurope61470.2024.10751608","title":"Anomaly Detection in Load Forecasting for Electric Vehicles Using Image Processing Techniques","year":2024,"lang":"en","type":"article","venue":"","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Anomaly detection; Computer science; Image processing; Anomaly (physics); Artificial intelligence; Computer vision; Image (mathematics); Pattern recognition (psychology)","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":[0.0003355276,0.0001764347,0.0001580892,0.0004205843,0.00005112298,0.0001758273,0.00009374142,0.0001061006,0.000006609838],"category_scores_gemma":[0.00007857315,0.0001756986,0.00005927037,0.0006562273,0.00001036349,0.0004665483,0.00001956687,0.0001966894,0.000001613374],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004116358,"about_ca_system_score_gemma":0.00003956653,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001344814,"about_ca_topic_score_gemma":0.0001630006,"domain_scores_codex":[0.9990625,0.00001107989,0.0002734376,0.0002340142,0.0001108541,0.0003081544],"domain_scores_gemma":[0.9997129,0.00009193926,0.00001843729,0.00009543684,0.00005242488,0.00002887417],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000003467638,0.000009593086,0.0001539032,0.0005172133,0.000005639286,0.000007394967,0.00005945717,0.0001445046,0.5651712,0.00002694486,0.0001307827,0.4337699],"study_design_scores_gemma":[0.00003030003,0.00002319748,0.00005079833,0.0001668507,0.000007132818,0.00001411322,0.000005967323,0.5180885,0.4805846,0.0004588472,0.0004507067,0.000118997],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5040572,0.001214521,0.4867163,0.00002119187,0.00005930933,0.0006111421,0.000002607012,0.004409197,0.002908559],"genre_scores_gemma":[0.9134415,0.00002915444,0.08607906,0.00001408606,0.00010634,0.0002394946,0.000001645359,0.00007363913,0.00001504635],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.517944,"threshold_uncertainty_score":0.7164782,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01759761057281543,"score_gpt":0.2947186355451524,"score_spread":0.277121024972337,"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."}}