{"id":"W4390480762","doi":"10.1109/access.2023.3348549","title":"A Deep Convolutional Neural Network-Based Approach to Detect False Data Injection Attacks on PV-Integrated Distribution Systems","year":2024,"lang":"en","type":"article","venue":"IEEE Access","topic":"Smart Grid Security and Resilience","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University; Concordia University","funders":"Qatar National Library","keywords":"Computer science; Convolutional neural network; Controller (irrigation); Smart grid; Photovoltaic system; Real-time computing; Deep learning; Voltage; Software deployment; Artificial intelligence; Engineering; Electrical engineering","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":[0.0002419112,0.0001972709,0.0001697871,0.00009368057,0.0001282215,0.0004015808,0.0005820858,0.0001318148,0.000006232121],"category_scores_gemma":[0.00003741356,0.0001732028,0.00004547435,0.0007849125,0.0000361788,0.0004424807,0.00005612894,0.0003466347,0.00005816808],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001894479,"about_ca_system_score_gemma":0.00004546947,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001414827,"about_ca_topic_score_gemma":0.00007752619,"domain_scores_codex":[0.9986654,0.00006606336,0.0002361247,0.0004194252,0.0002730242,0.0003399791],"domain_scores_gemma":[0.9992756,0.0001128288,0.00001993127,0.0004282387,0.00005213825,0.0001112624],"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":[0.00003484438,0.00001776959,0.000197567,0.0001536918,0.00002874199,0.00000891336,0.00002111981,0.9807125,0.0001466188,0.0001310946,0.01724163,0.001305559],"study_design_scores_gemma":[0.000131122,0.000047049,0.001075268,0.0001649388,0.00002177939,0.00002171833,0.00001326947,0.9909893,0.00038791,0.00001837898,0.006917527,0.0002117213],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1681336,0.001461131,0.8219668,0.00004197388,0.006579688,0.0004553188,0.0003143219,0.0007890176,0.0002581597],"genre_scores_gemma":[0.9979755,0.00001475501,0.00007187372,0.0000547964,0.0009472576,0.00007777911,0.0008139748,0.00002715977,0.00001686081],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8298419,"threshold_uncertainty_score":0.7063007,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03931393187846857,"score_gpt":0.2833904418676625,"score_spread":0.2440765099891939,"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."}}