Optimized Excitation Mode for Generalized Back Projection Algorithm in 3-D EIT

Wang, Hongbin; Xu, Guizhi; Zhang, Shuai; Yan, Weili

doi:10.1109/tmag.2014.2361771

Cited by 3 publications

References 9 publications

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Damage detection of carbon fiber reinforced polymer composite materials based on one-dimensional multi-scale residual convolution neural network

Min

Fan

et al. 2022

Review of Scientific Instruments

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Carbon fiber reinforced polymers (CFRPs) have been widely applied in the aerospace industry, and the health conditions of CFRPs largely affect aerospace safety. Due to the limitations of traditional detection methods, electrical impedance tomography (EIT) has been gradually applied in the damage detection of CFRP composite materials. Aiming at the problems of poor imaging quality and low identification rate in the traditional EIT reconstruction algorithm, an EIT algorithm based on the one-dimensional multi-scale residual convolution neural network (1D-MSK-ResNet) is proposed in this paper. A “voltage vector-conductivity media distribution” dataset is first established, and the training results of the testing dataset are used to verify and evaluate the algorithm. Simulation and experimental results indicated that the 1D-MSK-ResNet EIT algorithm could enhance the ability of damage identification and significantly improve the imaging quality.

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Damage detection of carbon fiber reinforced polymer composite materials based on one-dimensional multi-scale residual convolution neural network

Min

Fan

et al. 2022

Review of Scientific Instruments

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Multi-touch Recognition of Hydrogel-based E-skins using Real-world EIT Datasets

Nicholls,

Hardman,

Iida

2024

2024 IEEE 7th International Conference on Soft Robotics (RoboSoft)

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Ð We present a soft gelatin-based hydrogel e-skin capable of detecting up to six simultaneous tactile stimuli, using electrical impedance tomography (EIT) measurements and convolutional neural networks. Our networks are trained on only real-world data, for which we present two custom datacollecting end-effectors. These allow multi-touch responses to be measured quickly and autonomously (up to 8 seconds per datapoint), giving datasets more than 10× larger than those existing in the literature. To demonstrate the benefits of this approach, we train a non-homogeneous skin to predict 'macrobraille' patterns in a 3×2 grid, achieving a 89% classification accuracy.

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Review on Electrical Impedance Tomography: Artificial Intelligence Methods and its Applications

2019

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Electrical impedance tomography (EIT) has been a hot topic among researchers for the last 30 years. It is a new imaging method and has evolved over the last few decades. By injecting a small amount of current, the electrical properties of tissues are determined and measurements of the resulting voltages are taken. By using a reconstructing algorithm these voltages then transformed into a tomographic image. EIT contains no identified threats and as compared to magnetic resonance imaging (MRI) and computed tomography (CT) scans (imaging techniques), it is cheaper in cost as well. In this paper, a comprehensive review of efforts and advancements undertaken and achieved in recent work to improve this technology and the role of artificial intelligence to solve this non-linear, ill-posed problem are presented. In addition, a review of EIT clinical based applications has also been presented.

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Optimized Excitation Mode for Generalized Back Projection Algorithm in 3-D EIT

Cited by 3 publications

References 9 publications

Damage detection of carbon fiber reinforced polymer composite materials based on one-dimensional multi-scale residual convolution neural network

Damage detection of carbon fiber reinforced polymer composite materials based on one-dimensional multi-scale residual convolution neural network

Multi-touch Recognition of Hydrogel-based E-skins using Real-world EIT Datasets

Review on Electrical Impedance Tomography: Artificial Intelligence Methods and its Applications

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