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A machine-learning-based epistemic modeling framework for textile antenna design

Duygu Kan (UGent) , Domenico Spina (UGent) , Simon De Ridder (UGent) , Flavia Grassi, Hendrik Rogier (UGent) and Dries Vande Ginste (UGent)
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Abstract
A novel machine-learning-based framework to evaluate the effect of design parameters affected by epistemic uncertainty on the performance of textile antennas is presented in this letter. In particular, epistemic variations are characterized in the framework of possibility theory, which is combined with Bayesian optimization to accurately and efficiently perform uncertainty quantification. A suitable application example validates the proposed method.
Keywords
POLYNOMIAL CHAOS, MATHEMATICAL-THEORY, UNCERTAINTY, Bayesian optimization (BO), epistemic uncertainty, fuzzy variables, (FVs), Gaussian process (GP) textile antenna

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MLA
Kan, Duygu, et al. “A Machine-Learning-Based Epistemic Modeling Framework for Textile Antenna Design.” IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS, vol. 18, no. 11, 2019, pp. 2292–96.
APA
Kan, D., Spina, D., De Ridder, S., Grassi, F., Rogier, H., & Vande Ginste, D. (2019). A machine-learning-based epistemic modeling framework for textile antenna design. IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS, 18(11), 2292–2296.
Chicago author-date
Kan, Duygu, Domenico Spina, Simon De Ridder, Flavia Grassi, Hendrik Rogier, and Dries Vande Ginste. 2019. “A Machine-Learning-Based Epistemic Modeling Framework for Textile Antenna Design.” IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS 18 (11): 2292–96.
Chicago author-date (all authors)
Kan, Duygu, Domenico Spina, Simon De Ridder, Flavia Grassi, Hendrik Rogier, and Dries Vande Ginste. 2019. “A Machine-Learning-Based Epistemic Modeling Framework for Textile Antenna Design.” IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS 18 (11): 2292–2296.
Vancouver
1.
Kan D, Spina D, De Ridder S, Grassi F, Rogier H, Vande Ginste D. A machine-learning-based epistemic modeling framework for textile antenna design. IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS. 2019;18(11):2292–6.
IEEE
[1]
D. Kan, D. Spina, S. De Ridder, F. Grassi, H. Rogier, and D. Vande Ginste, “A machine-learning-based epistemic modeling framework for textile antenna design,” IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS, vol. 18, no. 11, pp. 2292–2296, 2019.
@article{8638548,
  abstract     = {A novel machine-learning-based framework to evaluate the effect of design parameters affected by epistemic uncertainty on the performance of textile antennas is presented in this letter. In particular, epistemic variations are characterized in the framework of possibility theory, which is combined with Bayesian optimization to accurately and efficiently perform uncertainty quantification. A suitable application example validates the proposed method.},
  author       = {Kan, Duygu and Spina, Domenico and De Ridder, Simon and Grassi, Flavia and Rogier, Hendrik and Vande Ginste, Dries},
  issn         = {1536-1225},
  journal      = {IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS},
  keywords     = {POLYNOMIAL CHAOS,MATHEMATICAL-THEORY,UNCERTAINTY,Bayesian optimization (BO),epistemic uncertainty,fuzzy variables,(FVs),Gaussian process (GP) textile antenna},
  language     = {eng},
  number       = {11},
  pages        = {2292--2296},
  title        = {A machine-learning-based epistemic modeling framework for textile antenna design},
  url          = {http://dx.doi.org/10.1109/LAWP.2019.2933306},
  volume       = {18},
  year         = {2019},
}

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