{"id":"W2049591644","doi":"10.1109/seconw.2014.6979705","title":"Harvesting-aware control of wireless sensor nodes using fuzzy logic and differential evolution","year":2014,"lang":"en","type":"article","venue":"","topic":"Energy Harvesting in Wireless Networks","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Fuzzy logic; Wireless sensor network; Controller (irrigation); Wireless; Control system; Real-time computing; Control (management); Fuzzy control system; Differential (mechanical device); Energy (signal processing); Distributed computing; Control engineering; Artificial intelligence; Computer network; Telecommunications; 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":[0.0002694383,0.0003135874,0.0002406309,0.0002009457,0.000233279,0.0003493197,0.0004608909,0.0003069027,0.0003271324],"category_scores_gemma":[0.0005644482,0.0001095297,0.0003034625,0.0001827971,0.000355574,0.0002584806,0.0003160697,0.000258594,0.00003880235],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003499864,"about_ca_system_score_gemma":0.0002607211,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00212253,"about_ca_topic_score_gemma":0.001567592,"domain_scores_codex":[0.9999075,0.00001981066,0.000005457198,0.00001884221,0.0000372599,0.00001104965],"domain_scores_gemma":[0.9998511,0.00007207193,0.00002169249,0.00001101135,0.00003606352,0.000007933196],"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":[0.0000665566,0.00006891851,0.001088593,0.00007003093,0.0000470026,0.0001774244,0.0001538711,0.872692,0.04652447,0.009206315,0.0002435073,0.0696613],"study_design_scores_gemma":[0.000007792742,0.00005694789,0.0001874835,0.000003066075,0.000007507053,0.00002281254,0.0000072221,0.995652,0.002642254,0.001121343,0.0002868225,0.000004682416],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.181493,0.0003488197,0.8128601,0.000119451,0.00005084769,0.00005521922,0.00001470769,0.0001431662,0.004914657],"genre_scores_gemma":[0.9680703,0.0001102467,0.03075726,0.0000232849,0.000005628071,0.00004409138,0.00001286176,0.00000453861,0.0009717877],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00212253,"threshold_uncertainty_score":0.004220307,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01040128382046753,"score_gpt":0.2007123033581982,"score_spread":0.1903110195377306,"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."}}