Nondestructive Evaluation of Buried Dielectric Cylinders by Asynchronous Particle Swarm Optimization

Lee, Wei-Tsong; Sun, Chih-Ming; Chiu, Chien‐Ching; Li, Jyun-Fu

doi:10.1520/jte20120323

Cited by 6 publications

(3 citation statements)

References 41 publications

(20 reference statements)

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“…Due to this feature, different forms of heuristic procedures have been adopted in the context of Structural Health Monitoring. Hunaidi [ 37 ] employs evolution-based Genetic Algorithms (GAs) for non-destructive assessment of pavements on the basis of surface waves tests; Farley et al [ 38 ] adopt an artificial neural network approach for defect detection via ultrasonic signals; Lee et al [ 39 ] formulate an inverse scattering problem on the basis of Particle Swarm Optimization; while Bernieri et al [ 40 ] reconstruct cracks via eddy current testing and a machine learning approach.…”

Section: Introductionmentioning

confidence: 99%

Multiple crack detection in 3D using a stable XFEM and global optimization

2018

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A numerical scheme is proposed for the detection of multiple cracks in three dimensional (3D) structures. The scheme is based on a variant of the extended finite element method (XFEM) and a hybrid optimizer solution. The proposed XFEM variant is particularly well-suited for the simulation of 3D fracture problems, and as such serves as an efficient solution to the so-called forward problem. A set of heuristic optimization algorithms are recombined into a multiscale optimization scheme. The introduced approach proves effective in tackling the complex inverse problem involved, where identification of multiple flaws is sought on the basis of sparse measurements collected near the structural boundary. The potential of the scheme is demonstrated through a set of numerical case studies of varying complexity.

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Section: Introductionmentioning

confidence: 99%

Multiple crack detection in 3D using a stable XFEM and global optimization

2018

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“…For buried objects under the frequency domain, most researchers applied conventional algorithms to solve the electromagnetic inverse scattering problems. In 2015, Lee proposed to use asynchronous particle swarm optimization to reconstruct permittivity of the twodimensional inhomogeneous dielectric cylinder [8]. In 2019, Chiu proposed to use a selfadaptive dynamic differential evolution method to reconstruct the periodic homogeneous dielectric object buried in rough surfaces [9].…”

Section: Introductionmentioning

confidence: 99%

Electromagnetic Imaging for Buried Conductors Using Deep Convolutional Neural Networks

Chiu

Chien

et al. 2023

Applied Sciences

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In the past, many conventional algorithms, such as self-adaptive dynamic differential evolution and asynchronous particle swarm optimization, were used to reconstruct buried objects in the frequency domain; these were unfortunately time-consuming during the iterative, repeated computing process of the scattered field. Consequently, we propose an innovative deep convolutional neural network approach to solve the electromagnetic inverse scattering problem for buried conductors in this paper. Different shapes of conductors are buried in one half-space and the electromagnetic wave from the other half-space is incident. The shape of the conductor can be reconstructed promptly by inputting the received scattered fields measured from the upper half-space into the deep convolutional neural network module, which avoids the computational complexity of Green’s function for training. Numerical results show that the root mean square error for differently shaped—circular, elliptical, arrow, peanut, four-petal, and three-petal—reconstructed images are, respectively, 2.95%, 3.11%, 17.81%, 15.10%, 14.14%, and 15.24%. Briefly speaking, not only can circular and elliptical buried conductors be reconstructed; some irregular shapes can be reconstructed well. On the contrary, the reconstruction result by U-Net for buried objects is worse since it is not able to obtain a good preliminary image by processing only the upper scattered field—that is, rather than the full space. In other words, our proposed deep convolutional neural network can efficiently solve the electromagnetic inverse scattering problem of buried conductors and provide a novel method for the microwave imaging of the buried conductors. This is the first successful attempt at using deep convolutional neural networks for buried conductors in the frequency domain, which may be useful for practical applications in various fields such as the medical, military, or industrial fields, including magnetic resonance imaging, mine detection and clearance, non-destructive testing, gas or wire pipeline detection, etc.

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“…To the best of the authors' knowledge, there is still no published work using asynchronous particle swarm optimization (APSO) and self-adaptive dynamic differential evolution (SADDE) to search for transmission locations to minimize the outage probability of indoor wireless communication channels. In this paper, two different optimization algorithms are proposed and compared to find the optimal transmitter location for the MIMO-UWB communication system [26][27][28]. The outage probability of the MIMO-UWB systems is investigated.…”

Section: Introductionmentioning

confidence: 99%

Optimal Location of the Access Points for MIMO-UWB Systems

Chien

Chiu

et al. 2018

Applied Sciences

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A multiple-input and multiple-output ultra-wideband (MIMO-UWB) system provides a higher data rate. However, the multipath effect of the intersymbol interference (ISI) increases the bit error rate (BER) and outage probability of the MIMO-UWB system. For this paper, the authors applied the real orthogonal design (ROD) to an MIMO-UWB system to improve the efficiency of that system. A ray-tracing technique and an inverse fast Fourier transform were used to get the impulse response of the indoor environment. In addition, a rake receiver was used to increase the strength of the received signal to minimize the multipath effect. For this paper, two cases of an indoor wireless MIMO-UWB system were studied: case (A) used different antenna arrays, whereas case (B) placed antenna arrays in different locations to find the best position of the transmitter. In case (A), three different shapes of antenna arrays, namely L-shape, circular-shape, and Y-shape, were used for the transmitter and receiver. The BER performance for these arrays in the UWB frequency of 3.1–10.6 GHz was examined. Numerical results showed that the outage probability of the circular array was better than that of the other two arrays. In case (B), the transmitter used was an array with two antenna elements. The optimal location for the transmitter was found by using both asynchronous particle swarm optimization (APSO) and self-adaptive dynamic differential evolution (SADDE). The numerical results indicated that the performance of APSO was better than that of SADDE.

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Nondestructive Evaluation of Buried Dielectric Cylinders by Asynchronous Particle Swarm Optimization

Cited by 6 publications

References 41 publications

Multiple crack detection in 3D using a stable XFEM and global optimization

Multiple crack detection in 3D using a stable XFEM and global optimization

Electromagnetic Imaging for Buried Conductors Using Deep Convolutional Neural Networks

Optimal Location of the Access Points for MIMO-UWB Systems

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