{"id":"W2117529974","doi":"10.1504/ijista.2010.030191","title":"Modelling a small-size unmanned helicopter using optimal estimation in the frequency domain","year":2009,"lang":"en","type":"article","venue":"International Journal of Intelligent Systems Technologies and Applications","topic":"Control Systems and Identification","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Shenyang Institute of Automation; National Natural Science Foundation of China","keywords":"Frequency domain; State-space representation; State space; Process (computing); Control theory (sociology); Domain (mathematical analysis); System identification; Identification (biology); Computer science; Time domain; Set (abstract data type); Estimation theory; Algorithm; Engineering; Data modeling; Mathematics; Artificial intelligence; Statistics; Computer vision","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":[0.0001454006,0.0005028146,0.000497714,0.0001901287,0.0002214394,0.0005327625,0.0004182202,0.0005493009,0.0009532365],"category_scores_gemma":[0.0003878795,0.0002768964,0.0003901796,0.0001271528,0.00036636,0.0007490663,0.0003679268,0.0005041781,0.0003073235],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000218359,"about_ca_system_score_gemma":0.0004451308,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005516045,"about_ca_topic_score_gemma":0.004321851,"domain_scores_codex":[0.9998738,0.00002547477,0.000006902839,0.00003369333,0.00004787788,0.00001221201],"domain_scores_gemma":[0.9998719,0.00004245808,0.00003350403,0.00001781936,0.0000285896,0.000005799749],"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":[0.00003953223,0.00001810722,0.0005719251,0.00006890467,0.00002248024,0.00009303619,0.00007582732,0.9713033,0.01309427,0.002361977,0.0001793466,0.01217121],"study_design_scores_gemma":[0.000004707909,0.00002690004,0.0002564327,0.000003462504,0.00000548092,0.00002156039,0.00001193297,0.9976579,0.001014962,0.000547063,0.0004454699,0.000004181455],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03806879,0.000111749,0.9592503,0.00006882264,0.00002221627,0.00003743296,0.00005606548,0.0002476135,0.002137081],"genre_scores_gemma":[0.9291626,0.0002720669,0.06713063,0.00004174227,0.00001858112,0.0001289392,0.0001518264,0.00003474494,0.003058829],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005516045,"threshold_uncertainty_score":0.01096791,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02008117200744884,"score_gpt":0.2546911894136376,"score_spread":0.2346100174061888,"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."}}