{"id":"W4413294059","doi":"10.1016/j.eswa.2025.129401","title":"Multi-UAV-aided power-up and data collection: Multi-agent DQL with genetic algorithm approach","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Energy Harvesting in Wireless Networks","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Institute for Information and Communications Technology Promotion; Korea Institute of Energy Technology Evaluation and Planning; Information Technology Research Centre; National Research Foundation of Korea; Ministry of Science and ICT, South Korea; Ministry of Trade, Industry and Energy","keywords":"Computer science; Genetic algorithm; Power (physics); Data collection; Data mining; Algorithm; Artificial intelligence; Machine learning; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007587716,0.000532001,0.00110879,0.0005579144,0.0005305609,0.0009608352,0.001089436,0.001057428,0.001597794],"category_scores_gemma":[0.001522242,0.0004196513,0.0005798556,0.0006191228,0.0004705242,0.0007297015,0.001008685,0.0006689018,0.0002713889],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006358393,"about_ca_system_score_gemma":0.0009831032,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007096912,"about_ca_topic_score_gemma":0.005555314,"domain_scores_codex":[0.9996619,0.0001010982,0.00001786888,0.00006459868,0.0001064319,0.0000480886],"domain_scores_gemma":[0.9994783,0.0002630681,0.00006799598,0.00003438099,0.0001284008,0.00002784828],"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":[0.0000622366,0.00006933103,0.0004946167,0.00006839383,0.00004328809,0.00007846725,0.00006736843,0.9403608,0.002932751,0.003298541,0.0005140296,0.05201015],"study_design_scores_gemma":[0.000006521163,0.00001960354,0.00005093589,0.000002929219,0.000004253603,0.000008508631,0.000007528746,0.999012,0.0003122628,0.0004237671,0.0001493936,0.000002302804],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01652534,0.0001652165,0.9796674,0.0001222299,0.0000377218,0.00005461277,0.00002083283,0.0002086489,0.003197971],"genre_scores_gemma":[0.7854852,0.0001327654,0.2116282,0.0001207538,0.00002680307,0.0002006487,0.00005658683,0.00004504815,0.002304031],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007096912,"threshold_uncertainty_score":0.01411122,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02210736225022162,"score_gpt":0.2581538054308554,"score_spread":0.2360464431806338,"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."}}