Probabilistic cracking prediction via deep learned electrical tomography

In recent years, electrical tomography, namely, electrical resistance tomography (ERT), has emerged as a viable approach to detecting, localizing and reconstructing structural cracking patterns in concrete structures. High-fidelity ERT reconstructions, however, often require computationally expensive optimization regimes and complex constraining and regularization schemes, which impedes pragmatic implementation in Structural Health Monitoring frameworks. To address this challenge, this article proposes the use of predictive deep neural networks to directly and rapidly solve an analogous ERT inverse problem. Specifically, the use of cross-entropy loss is used in optimizing networks forming a nonlinear mapping from ERT voltage measurements to binary probabilistic spatial crack distributions (cracked/not cracked). In this effort, artificial neural networks and convolutional neural networks are first trained using simulated electrical data. Following, the feasibility of the predictive networks is tested and affirmed using experimental and simulated data considering flexural and shear cracking patterns observed from reinforced concrete elements.

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Data-driven modeling for boiling heat transfer: Using deep neural networks and high-fidelity simulation results

Applied Thermal Engineering ◽

10.1016/j.applthermaleng.2018.08.041 ◽

2018 ◽

Vol 144 ◽

pp. 305-320 ◽

Cited By ~ 18

Author(s):

Yang Liu ◽

Nam Dinh ◽

Yohei Sato ◽

Bojan Niceno

Keyword(s):

Heat Transfer ◽

Neural Networks ◽

Boiling Heat Transfer ◽

Deep Neural Networks ◽

Data Driven ◽

High Fidelity ◽

High Fidelity Simulation ◽

Simulation Results ◽

Data Driven Modeling

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High-Fidelity Modeling of Light-Duty Vehicle Emission and Fuel Economy Using Deep Neural Networks

10.4271/2021-01-0181 ◽

2021 ◽

Author(s):

Farhang Motallebiaraghi ◽

Aaron Rabinowitz ◽

Shantanu Jathar ◽

Alvis Fong ◽

Zachary Asher ◽

...

Keyword(s):

Neural Networks ◽

Fuel Economy ◽

Deep Neural Networks ◽

Vehicle Emission ◽

High Fidelity ◽

Light Duty ◽

Light Duty Vehicle

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Novel U-net based deep neural networks for transmission tomography

Journal of X-Ray Science and Technology ◽

10.3233/xst-210962 ◽

2021 ◽

pp. 1-19

Author(s):

Csaba Olasz ◽

László G. Varga ◽

Antal Nagy

Keyword(s):

Neural Networks ◽

Deep Neural Networks ◽

Tomographic Reconstruction ◽

Similarity Index ◽

Simulated Data ◽

Electrical Noise ◽

Projection Data ◽

Beam Hardening ◽

The Neural Networks

BACKGROUND: The fusion of computer tomography and deep learning is an effective way of achieving improved image quality and artifact reduction in reconstructed images. OBJECTIVE: In this paper, we present two novel neural network architectures for tomographic reconstruction with reduced effects of beam hardening and electrical noise. METHODS: In the case of the proposed novel architectures, the image reconstruction step is located inside the neural networks, which allows the network to be trained by taking the mathematical model of the projections into account. This strong connection enables us to enhance the projection data and the reconstructed image together. We tested the two proposed models against three other methods on two datasets. The datasets contain physically correct simulated data, and they show strong signs of beam hardening and electrical noise. We also performed a numerical evaluation of the neural networks on the reconstructed images according to three error measurements and provided a scoring system of the methods derived from the three measures. RESULTS: The results showed the superiority of the novel architecture called TomoNet2. TomoNet2 improved the quality of the images according to the average Structural Similarity Index from 0.9372 to 0.9977 and 0.9519 to 0.9886 on the two data sets, when compared to the FBP method. This network also yielded the best results for 79.2 and 53.0 percent for the two datasets according to Peak-Signal-to-Noise-Ratio compared to the other improvement techniques. CONCLUSIONS: Our experimental results showed that the reconstruction step used in skip connections in deep neural networks improves the quality of the reconstructions. We are confident that our proposed method can be effectively applied to other datasets for tomographic purposes.

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CDAE: A Cascade of Denoising Autoencoders for Noise Reduction in the Clustering of Single-Particle Cryo-EM Images

Frontiers in Genetics ◽

10.3389/fgene.2020.627746 ◽

2021 ◽

Vol 11 ◽

Author(s):

Houchao Lei ◽

Yang Yang

Keyword(s):

Neural Networks ◽

Noise Reduction ◽

Image Denoising ◽

Single Particle ◽

Deep Neural Networks ◽

Simulated Data ◽

Real Data ◽

Image Features ◽

Data Set ◽

New Model

As an emerging technology, cryo-electron microscopy (cryo-EM) has attracted more and more research interests from both structural biology and computer science, because many challenging computational tasks are involved in the processing of cryo-EM images. An important image processing step is to cluster the 2D cryo-EM images according to their projection angles, then the cluster mean images are used for the subsequent 3D reconstruction. However, cryo-EM images are quite noisy and denoising them is not easy, because the noise is a complicated mixture from samples and hardware. In this study, we design an effective cryo-EM image denoising model, CDAE, i.e., a cascade of denoising autoencoders. The new model comprises stacked blocks of deep neural networks to reduce noise in a progressive manner. Each block contains a convolutional autoencoder, pre-trained by simulated data of different SNRs and fine-tuned by target data set. We assess this new model on three simulated test sets and a real data set. CDAE achieves very competitive PSNR (peak signal-to-noise ratio) in the comparison of the state-of-the-art image denoising methods. Moreover, the denoised images have significantly enhanced clustering results compared to original image features or high-level abstraction features obtained by other deep neural networks. Both quantitative and visualized results demonstrate the good performance of CDAE for the noise reduction in clustering single-particle cryo-EM images.

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