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  "Title": "Machine Learning-Based Analysis of Potential Power Gain from\nPassive Device Installation on Wind Turbine Generators",
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  "Authors@R": "c(\nperson(\"Hoon\", \"Hwangbo\", email = \"hhwangb1@utk.edu\", role = c(\"aut\",\"cre\")),\nperson(\"Yu\", \"Ding\", email = \"yuding@tamu.edu\", role = c(\"aut\")),\nperson(\"Daniel\", \"Cabezon\", email = \"Daniel.Cabezon@edpr.com\", role = c(\"aut\")),\nperson(\"Texas A&M University\", role = c(\"cph\")),\nperson(\"EDP Renewables\", role = c(\"cph\")))",
  "Author": "Hoon Hwangbo [aut, cre], Yu Ding [aut], Daniel Cabezon [aut],\nTexas A&M University [cph], EDP Renewables [cph]",
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  "Copyright": "Copyright (c) 2019 Y. Ding, H. Hwangbo, Texas A&M\nUniversity, D. Cabezon, and EDP Renewables",
  "Description": "Provides an effective machine learning-based tool that\nquantifies the gain of passive device installation on wind\nturbine generators. H. Hwangbo, Y. Ding, and D. Cabezon (2019)\n<arXiv:1906.05776>.",
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