{"id":"W3040694753","doi":"10.1109/tii.2020.3008223","title":"A Data-Driven Auto-CNN-LSTM Prediction Model for Lithium-Ion Battery Remaining Useful Life","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Informatics","topic":"Advanced Battery Technologies Research","field":"Engineering","cited_by":663,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; St. Francis Xavier University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Big data; Autoencoder; Process (computing); Deep learning; Data modeling; Convolutional neural network; Battery (electricity); Artificial intelligence; SPARK (programming language); Convolution (computer science); Artificial neural network; Key (lock); Data mining; Machine learning","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":[0.0002305815,0.000285235,0.0003330879,0.0002641928,0.0002213261,0.0001131397,0.0006845298,0.0004906355,0.00003592268],"category_scores_gemma":[0.0001617727,0.0003011682,0.0000911421,0.0004929771,0.00007507664,0.001418204,0.00001583086,0.001256448,0.00006590087],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002030778,"about_ca_system_score_gemma":0.0001408408,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001522371,"about_ca_topic_score_gemma":0.00000343617,"domain_scores_codex":[0.9980115,0.00002089662,0.0008585529,0.0002207438,0.0003996255,0.0004886835],"domain_scores_gemma":[0.9986505,0.0002293189,0.0001087914,0.0007008681,0.00008267537,0.0002278343],"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":[0.0001298957,0.00002386639,0.000004638438,0.0001082648,0.00009454869,7.412538e-7,0.0007981159,0.9727201,0.0003955036,0.000008193314,0.0131442,0.01257193],"study_design_scores_gemma":[0.001293455,0.0002539069,0.000001277805,0.00007480413,0.00005069837,0.000003143545,0.0006094666,0.9872545,0.005006765,0.00003425654,0.005160271,0.0002574477],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01255607,0.000005767421,0.9811692,0.0007541812,0.0008209985,0.0008700393,0.001957923,0.001623804,0.0002419909],"genre_scores_gemma":[0.9693294,0.000106297,0.02871638,0.0006179982,0.0004813941,0.0002803075,0.0002600391,0.00010886,0.00009938441],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9567733,"threshold_uncertainty_score":0.999944,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1702212134600097,"score_gpt":0.3022400719237633,"score_spread":0.1320188584637536,"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."}}