{"id":"W2964050632","doi":"10.1109/icra.2017.7989607","title":"Deep neural networks for improved, impromptu trajectory tracking of quadrotors","year":2017,"lang":"en","type":"article","venue":"","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":91,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dynamic Systems Analysis (Canada)","funders":"","keywords":"Trajectory; Computer science; Controller (irrigation); Tracking (education); Control theory (sociology); Artificial neural network; PID controller; Nonlinear system; Artificial intelligence; Control engineering; Control (management); Engineering","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":[0.0003380226,0.0005212888,0.0003228998,0.0003029665,0.0001806215,0.0003396918,0.0005174421,0.0004257342,0.001484421],"category_scores_gemma":[0.0009386333,0.0002745824,0.0002637956,0.0003230741,0.0002421654,0.0005007469,0.0004521965,0.0006663151,0.0002815397],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007569297,"about_ca_system_score_gemma":0.0004521378,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01114737,"about_ca_topic_score_gemma":0.0120558,"domain_scores_codex":[0.9999092,0.00001132451,0.00000678632,0.0000316462,0.00002426963,0.00001673293],"domain_scores_gemma":[0.9998288,0.00006515403,0.00002759598,0.00002158546,0.00004503058,0.00001173701],"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":[0.0001517551,0.00004638012,0.0007925642,0.00009291911,0.00004129454,0.0001030415,0.00006560911,0.7342591,0.0230553,0.002535864,0.001353253,0.2375028],"study_design_scores_gemma":[0.000002073718,0.0000141537,0.0001624148,0.000003861857,0.000002992443,0.000008905908,0.000003362941,0.9966876,0.002315248,0.0004549679,0.000342311,0.000002185454],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0576292,0.0009369459,0.9368495,0.000164667,0.0000855168,0.00003770637,0.0001179525,0.001610905,0.002567486],"genre_scores_gemma":[0.8745522,0.0004054728,0.1205371,0.0000905553,0.00001928068,0.00004560725,0.0002392212,0.00009470114,0.004015785],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01114737,"threshold_uncertainty_score":0.022165,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0421423574292548,"score_gpt":0.2994822501827175,"score_spread":0.2573398927534626,"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."}}