{"id":"W2126450366","doi":"10.36001/phmconf.2010.v2i1.1896","title":"An Adaptive Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-ion Batteries","year":2010,"lang":"en","type":"article","venue":"Annual Conference of the PHM Society","topic":"Advanced Battery Technologies Research","field":"Engineering","cited_by":252,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University; Carleton University","funders":"","keywords":"Prognostics; Artificial neural network; Computer science; Recurrent neural network; State of health; Service life; Lithium (medication); Reliability engineering; Artificial intelligence; Machine learning; Engineering; Data mining; Battery (electricity)","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":[0.0002587497,0.0001368509,0.000204727,0.0000182419,0.0001037785,0.00001803527,0.0004813296,0.0001590138,0.00001493102],"category_scores_gemma":[0.0001778467,0.0001094449,0.0001343899,0.0001696831,0.0003066408,0.000338945,0.0001349471,0.0004917615,4.944048e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002400987,"about_ca_system_score_gemma":0.00004251225,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007271538,"about_ca_topic_score_gemma":0.00001206884,"domain_scores_codex":[0.9990453,0.00003056011,0.0002569353,0.0001735698,0.0002020681,0.0002916216],"domain_scores_gemma":[0.9989566,0.0001336748,0.0001032667,0.000420693,0.0003366116,0.00004911009],"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":[0.0006766213,0.0003799158,0.0462576,0.001265782,0.0008294433,0.000001205479,0.04796389,0.1461772,0.5882027,0.0103758,0.05163546,0.1062343],"study_design_scores_gemma":[0.001153657,0.001849051,0.05618451,0.0003732425,0.00008642366,0.000007546289,0.0212734,0.7763547,0.128611,0.008041452,0.005423849,0.0006412788],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9782528,0.00004170376,0.01959888,0.0003390108,0.0007427614,0.0003688707,0.0003406665,0.0002037659,0.0001115483],"genre_scores_gemma":[0.9928268,0.00004737676,0.006847254,0.00002665053,0.0001520619,0.00004401688,0.00001539577,0.00002136583,0.00001904588],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6301774,"threshold_uncertainty_score":0.4463032,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04372108605268748,"score_gpt":0.281104038336771,"score_spread":0.2373829522840835,"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."}}