{"id":"W4321072562","doi":"10.1016/j.engfailanal.2023.107132","title":"Automatic detection of deteriorated photovoltaic modules using IRT images and deep learning (CNN, LSTM) strategies","year":2023,"lang":"en","type":"article","venue":"Engineering Failure Analysis","topic":"Photovoltaic System Optimization Techniques","field":"Energy","cited_by":34,"is_retracted":false,"has_abstract":false,"ca_institutions":"Saint Mary's University","funders":"","keywords":"Photovoltaic system; Convolutional neural network; Overheating (electricity); Computer science; Artificial intelligence; Fault detection and isolation; Artificial neural network; Deep learning; Drone; Pattern recognition (psychology); Computation; Real-time computing; Engineering; Electrical engineering; Algorithm","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":[0.0002944098,0.0002426236,0.0005093251,0.001330307,0.0001000253,0.0001145021,0.0001226978,0.0001379862,0.00005631814],"category_scores_gemma":[0.0001176885,0.0002554304,0.0001588496,0.003370015,0.00003521981,0.0003357736,0.00005827421,0.000146036,0.0000064168],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007538529,"about_ca_system_score_gemma":0.00001710547,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001800516,"about_ca_topic_score_gemma":0.0004205086,"domain_scores_codex":[0.9986384,0.00007989838,0.0004700095,0.0003034088,0.0002375592,0.000270713],"domain_scores_gemma":[0.9992289,0.0000988161,0.0002028136,0.0002688371,0.0001339383,0.00006668882],"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":[0.000001474963,0.000003419465,0.0005992483,0.00007125772,0.0003459168,0.000003753399,0.0001524829,0.5245317,0.4730306,0.00001210312,0.000001682767,0.001246301],"study_design_scores_gemma":[0.00009456995,0.00002275938,0.002933039,0.00004612857,0.0004046486,0.000004727004,0.0005299782,0.8076699,0.1879699,0.00002798688,0.00009946805,0.0001968926],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7422702,0.0001573757,0.256157,0.000003420036,0.00003205978,0.000106447,0.000002856286,0.001232205,0.00003848923],"genre_scores_gemma":[0.9810552,0.00006516559,0.01869965,0.000001983188,0.00002445469,0.00003513572,0.00003313606,0.00005110945,0.00003418107],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2850608,"threshold_uncertainty_score":0.9999898,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007044501457888066,"score_gpt":0.2202859367771187,"score_spread":0.2132414353192306,"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."}}