{"id":"W2786801842","doi":"10.1109/vtcfall.2017.8288323","title":"Vehicle-to-Grid Frequency Regulation Signal Optimization Based on Inhomogeneous Hidden Markov Model","year":2017,"lang":"en","type":"article","venue":"","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Hidden Markov model; Grid; Decoupling (probability); Markov chain; Transmission (telecommunications); Real-time computing; SIGNAL (programming language); Markov model; Service provider; Mathematical optimization; Reliability engineering; Service (business); Telecommunications; Engineering; Control engineering; Artificial intelligence; Mathematics","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":[0.0007164109,0.0006702697,0.001214919,0.0003420123,0.0003861516,0.0006743916,0.00104259,0.0006995535,0.001515293],"category_scores_gemma":[0.001648975,0.0004490093,0.0006981582,0.0004607045,0.000658275,0.0006571622,0.0006268923,0.00112707,0.0002002984],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001129289,"about_ca_system_score_gemma":0.001230841,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0312979,"about_ca_topic_score_gemma":0.01991122,"domain_scores_codex":[0.9995813,0.0001114745,0.00002038227,0.000116882,0.0000743513,0.00009566236],"domain_scores_gemma":[0.9990521,0.000644185,0.0001121427,0.00002911436,0.000118984,0.00004347059],"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":[0.00002713987,0.000008330781,0.0003776554,0.000009031936,0.00001004389,0.00002085205,0.00001293419,0.9943973,0.0002435654,0.002090065,0.0001067825,0.002696374],"study_design_scores_gemma":[0.000001977579,0.000003444581,0.00005536104,4.93989e-7,0.000002210142,0.000001423878,0.00000127966,0.9994828,0.00004349588,0.0003859154,0.00002030835,0.000001311013],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.092337,0.0003617747,0.9032086,0.0003152752,0.00004971625,0.00004319777,0.0001561301,0.0003747081,0.003153603],"genre_scores_gemma":[0.98164,0.000151126,0.01600571,0.00005380358,0.0000207516,0.00004794288,0.0001557999,0.00002363389,0.001901209],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0312979,"threshold_uncertainty_score":0.06223142,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007226587862651898,"score_gpt":0.2018481913800955,"score_spread":0.1946216035174436,"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."}}