Solutions to electromagnetic compatibility problems using artificial neural networks representation of vector finite element method

Salameh, M. S. Al; Zuraiqi, E.T. Al

doi:10.1049/iet-map:20060189

Cited by 7 publications

(3 citation statements)

References 19 publications

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“…Electromagnetic compatibility (EMC) [1][2][3][4] has been a popular research topic in recent years. As a main object of EMC study, the analysis of electromagnetic interference (EMI) is of significant importance.…”

Section: Introductionmentioning

confidence: 99%

Reconstruction and basis function construction of electromagnetic interference source signals based on Toeplitz‐based singular value decomposition

Zhao

et al. 2017

IET signal process.

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In this study, the authors propose a novel method, namely Toeplitz-based singular value decomposition (or TL-SVD for short) for the reconstruction and basis function construction of electromagnetic interference (EMI) source signals. Given a specific EMI source signal, they first construct a Toeplitz type data matrix. By applying singular value decomposition (SVD) to the constructed matrix, they obtain a set of singular values, which are further divided into two parts, corresponding to the clear and noisy components of input signals, respectively. The de-noised signal can then be reconstructed by reserving relatively larger singular values and abandoning smaller ones. Finally, by utilising the compositions of certain vectors resulting from the previous SVD step, the basis function can be constructed. To evaluate the performance of the proposed method, they conduct extensive experiments on both the synthetic data and real EMI signals, by comparing with several state-of-the-art signal reconstruction methods, such as discrete wavelet transform, EEMD, sparse representation based on K-SVD and OMP. Experimental results demonstrate that the proposed method can outperform comparison approaches.

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Section: Introductionmentioning

confidence: 99%

Reconstruction and basis function construction of electromagnetic interference source signals based on Toeplitz‐based singular value decomposition

Zhao

et al. 2017

IET signal process.

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“…ANNs have been exploited in different EMC problems such as detection and identification of vehicles based on their unintended radiated emissions [23], target discrimination [24,25], calculation of multilayer magnetic shielding [26], estimating PCB configuration from EMI measurements [27], modeling of the integrated circuits immunity to conducted electromagnetic disturbances [28], recognition and identification of radiated EMI for shielding apertures [29], prediction of electromagnetic field in metallic enclosures [30], adaptive beamforming [31,32], PAD modeling [33], cross talk on PCB & radar cross-section of cylinders with apertures [34], and detection of dielectric cylinders buried in a lossy half-space [35]. The computational capabilities of ANNs have been already utilized to estimate exhaust concentrations as a function of traffic and meteorological variables in [36].…”

Section: Introductionmentioning

confidence: 99%

On-Road Magnetic Emissions Prediction of Electric Cars in Terms of Driving Dynamics Using Neural Networks

Wefky¹,

Espinosa²,

Leferink³

et al. 2013

PIER

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Abstract-This paper presents a novel artificial neural network (ANN) model estimating vehicle-level radiated magnetic emissions of an electric car as a function of the corresponding driving pattern. Real world electromagnetic interference (EMI) experiments have been realized in a semi-anechoic chamber using Renault Twizy. Time-domain electromagnetic interference (TDEMI) measurement techniques have been employed to record the radiated disturbances in the 150 kHz-30 MHz range. Interesting emissions have been found in the range 150 kHz-3.8 MHz approximately. The instantaneous vehicle speed and acceleration have been chosen to represent the vehicle operational modes. A comparative study of the prediction performance between different static and dynamic neural networks has been done. Results showed that a Multilayer Perceptron (MLP) model trained with extreme learning machines (ELM) has achieved the best prediction results. The proposed model has been used to estimate the radiated magnetic field levels of an urban trip carried out with a Think City electric car.

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“…Nodal finite-element model was embedded in a NN architecture that enables solution of simple electromagnetic forward and inverse problems; a gradient descent algorithm was used even the authors expected that using conjugate gradient will improve solution speed [8]. NNs have been combined with the vector finite element method for forward problems [9].…”

Section: Introductionmentioning

confidence: 99%

Electromagnetic non‐destructive evaluation by vector finite‐element neural networks

Salameh

Taha²

2011

Int J Numerical Modelling

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SUMMARYThis study proposes neural network-based iterative inverse solutions for non-destructive evaluation (NDE) in which vector finite elements (VFEM) represent the forward model that closely models the physical process. The iterative algorithm can eventually estimate the material parameters. Vector finite element method global matrix is stored in a compact form using its sparsity and symmetry. The stored matrix elements are employed as the neurons weights, and preconditioning techniques are used to accelerate convergence of the neural networks (NN) algorithm. Detailed algorithm describing this new method is given to facilitate implementation. Combining vector finite elements and NNs offers several advantages over each technique alone, such as reducing memory storage requirements and the easily computed fixed weights of the NN.Various examples are solved to show the performance and usefulness of the proposed method, including lossy printed circuit board and lossy inhomogeneous cylindrical problems with ferromagnetic materials. These solutions compare very well with other published data where the maximum relative error was 5%.

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Solutions to electromagnetic compatibility problems using artificial neural networks representation of vector finite element method

Cited by 7 publications

References 19 publications

Reconstruction and basis function construction of electromagnetic interference source signals based on Toeplitz‐based singular value decomposition

Reconstruction and basis function construction of electromagnetic interference source signals based on Toeplitz‐based singular value decomposition

On-Road Magnetic Emissions Prediction of Electric Cars in Terms of Driving Dynamics Using Neural Networks

Electromagnetic non‐destructive evaluation by vector finite‐element neural networks

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