{"id":"W4220750544","doi":"10.3390/en15062288","title":"State Estimation Fusion for Linear Microgrids over an Unreliable Network","year":2022,"lang":"en","type":"article","venue":"Energies","topic":"Smart Grid Security and Resilience","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Agencia Estatal de Investigación; Universidad Carlos III de Madrid; Ministerio de Ciencia e Innovación; Comunidad de Madrid","keywords":"Computer science; Network packet; Lossy compression; Kalman filter; Linear regression; Packet loss; Mean squared error; Perceptron; Random forest; Sensor fusion; Multilayer perceptron; Fusion; Data mining; Real-time computing; Artificial intelligence; Machine learning; Artificial neural network; Statistics; Mathematics; Computer network","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":[0.001253446,0.0006063814,0.0007316571,0.000333091,0.0004328846,0.0006293347,0.000511741,0.0003537386,0.0007746872],"category_scores_gemma":[0.002375901,0.000243434,0.0005171201,0.000504847,0.0004160235,0.001099675,0.0006912075,0.0006269875,0.0001405192],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000543572,"about_ca_system_score_gemma":0.0005946587,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008844286,"about_ca_topic_score_gemma":0.005300449,"domain_scores_codex":[0.9995529,0.0001379233,0.00003623573,0.0001283556,0.0000943715,0.00005014413],"domain_scores_gemma":[0.9993363,0.0003326406,0.0001391987,0.00005821809,0.0001199065,0.00001372537],"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":[0.00009991314,0.00002447489,0.001328531,0.00006701003,0.00004157451,0.00006595685,0.00007187742,0.9508828,0.002275908,0.001272402,0.0002288758,0.04364074],"study_design_scores_gemma":[0.000001970586,0.00001776453,0.0003235679,0.000002408911,0.000006541208,0.000004408294,0.000009768688,0.9984285,0.0005495775,0.0005745175,0.00007770682,0.000003326025],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1372693,0.0004409981,0.8598386,0.0001859234,0.00004210901,0.00003343997,0.00008270452,0.0006137468,0.001493144],"genre_scores_gemma":[0.9825851,0.0001262678,0.01669857,0.00002284725,0.00001305933,0.00002273161,0.00006238808,0.00001171463,0.0004574275],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008844286,"threshold_uncertainty_score":0.01758564,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006554675157147589,"score_gpt":0.2208415645724579,"score_spread":0.2142868894153103,"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."}}