{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000261214,0.0009180707,0.0005501051,0.001193308,0.0001661219,0.0005857618,0.0005926379,0.0005965985,0.00160044],"category_scores_gemma":[0.0005928803,0.0002030371,0.0005265092,0.0005890887,0.0001800944,0.0005925543,0.0003856081,0.0004631302,0.0007146598],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004230076,"about_ca_system_score_gemma":0.0002609949,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001928705,"about_ca_topic_score_gemma":0.00402364,"domain_scores_codex":[0.9998596,0.0000102526,0.000006947425,0.00004088095,0.00005495234,0.00002739544],"domain_scores_gemma":[0.999737,0.00003853722,0.00006589581,0.00002831539,0.0001159447,0.00001432396],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006450475,0.0002238317,0.02183394,0.0004854601,0.0001569801,0.001000181,0.0001265292,0.1032794,0.2436352,0.001167962,0.007231982,0.6202134],"study_design_scores_gemma":[0.000009631843,0.0001145667,0.02196335,0.00003724232,0.0000714073,0.0002933915,0.00007263853,0.9161136,0.05860336,0.001302718,0.001393944,0.00002416967],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3558,0.001155279,0.6316341,0.0002610436,0.000144791,0.00009350644,0.0007107171,0.00462034,0.00558014],"genre_scores_gemma":[0.9456218,0.0003035611,0.05029145,0.0000685235,0.00003381023,0.00002747122,0.0004175759,0.0001122209,0.003123605],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001928705,"threshold_uncertainty_score":0.005354047,"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."}}