Generalized Gamma-Laguerre Polynomial Chaos to Model Random Bending of Wearable Antennas

Rogier, Hendrik

doi:10.1109/lawp.2022.3162688

Cited by 4 publications

(2 citation statements)

References 27 publications

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“…Polynomial chaos expansion (PCE), a prominent method for uncertainty quantification (UQ) in various engineering fields [1]- [18], gradually shows a significant decrease in its efficiency as the count of random parameters increases, i.e., the curse of dimensionality.…”

Section: Introductionmentioning

confidence: 99%

Automatic Determination of Set of Multivariate Basis Polynomials Based on Recursion and Application of LSPCR to High-Dimensional Uncertainty Quantification of Multi-Conductor Transmission Lines

Chen,

Ji,

et al. 2024

IEEE Access

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Uncertainty quantification (UQ) in polynomial chaos expansion (PCE) suffers from the curse of dimensionality, which is fundamentally reflected in that the number of the PCE coefficients to be found grows rapidly as the number of random inputs increases, and thus the number of samples required increases dramatically. Regardless of the approach taken to alleviate this disaster, we are usually faced with the technical challenge of automatically and programmatically determining the basis functions during the PCE implementation, which are composed of a large number of multivariate polynomials. To address this problem, this paper proposes an algorithm based on the recursive idea for automatically determining the basis functions, and combines this algorithm with the latest weight-based regression called least squares polynomial chaos regression (LSPCR), proposes the implementation of the LSPCR method in the medium-and high-dimensional cases, and evaluates its performance in the electromagnetic coupling problem of the transmission lines (TLs) in this case. Subsequently, the performance of six algorithms for LSPCR are compared in the medium-and high-dimensional, low-order cases, each algorithm consisting of a sampling strategy selected from asymptotic sampling (AS), standard sampling (SS), and coherence-optimal sampling (COS), paired with a norm problem chosen from either least squares optimization (LSO) or 𝓵 𝟏 -minimization (𝓵 𝟏 -M) problems. Numerical experiments demonstrate the excellence of the proposed algorithm. Furthermore, the results also show that only the algorithms that employ the SS (SSs) and the algorithms based on the COS (COSs) require no more than 1.4% of the total Monte Carlo (MC) computation time when producing similarly accurate results to the MC, regardless of the chosen norm problem.INDEX TERMS uncertainty quantification (UQ), polynomial chaos expansion (PCE), least squares polynomial chaos regression, high dimensionality, transmission lines (TLs). I.

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Section: Introductionmentioning

confidence: 99%

Automatic Determination of Set of Multivariate Basis Polynomials Based on Recursion and Application of LSPCR to High-Dimensional Uncertainty Quantification of Multi-Conductor Transmission Lines

Chen,

Ji,

et al. 2024

IEEE Access

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“…Therefore, a fitting stochastic framework for the characterization of uncertainty propagation within these systems is Generalized Polynomial Chaos (gPC) [ 2 , 3 ]. As a versatile technique, gPC has been applied extensively to study the effects of randomness on antenna performance and radio wave propagation [ 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 ]. With the appropriate approach, gPC can even compete with MC when a high number of random variables are used [ 12 , 13 , 14 , 15 , 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

A New Conformal Map for Polynomial Chaos Applied to Direction-of-Arrival Estimation via UCA Root-MUSIC

Brandt

Verhaevert

Hecke

et al. 2022

Sensors

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The effects of random array deformations on Direction-of-Arrival (DOA) estimation with root-Multiple Signal Classification for uniform circular arrays (UCA root-MUSIC) are characterized by a conformally mapped generalized Polynomial Chaos (gPC) algorithm. The studied random deformations of the array are elliptical and are described by different Beta distributions. To successfully capture the erratic deviations in DOA estimates that occur at larger deformations, specifically at the edges of the distributions, a novel conformal map is introduced, based on the hyperbolic tangent function. The application of this new map is compared to regular gPC and Monte Carlo sampling as a reference. A significant increase in convergence rate is observed. The numerical experiments show that the UCA root-MUSIC algorithm is robust to the considered array deformations, since the resulting errors on the DOA estimates are limited to only 2 to 3 degrees in most cases.

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Uncertainty propagation of flutter derivatives and structural damping in buffeting fragility analysis of long-span bridges using surrogate models

Hu,

Fang,

2024

Structural Safety

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Generalized Gamma-Laguerre Polynomial Chaos to Model Random Bending of Wearable Antennas

Cited by 4 publications

References 27 publications

Automatic Determination of Set of Multivariate Basis Polynomials Based on Recursion and Application of LSPCR to High-Dimensional Uncertainty Quantification of Multi-Conductor Transmission Lines

Automatic Determination of Set of Multivariate Basis Polynomials Based on Recursion and Application of LSPCR to High-Dimensional Uncertainty Quantification of Multi-Conductor Transmission Lines

A New Conformal Map for Polynomial Chaos Applied to Direction-of-Arrival Estimation via UCA Root-MUSIC

Uncertainty propagation of flutter derivatives and structural damping in buffeting fragility analysis of long-span bridges using surrogate models

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