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Visualization-based prediction of dendritic copper growth in electrochemical cells using convolutional long short-term memory

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能源与人工智能(英文) 2022年第4期10卷 149-160页

作者： Roshan Kumar Trina Dhara Han Hu Monojit Chakraborty Department of Chemical Engineering Indian Institute of Technology KharagpurKharagpur721302India Department of Mechanical Engineering University of ArkansasFayettevilleAR72701United States of America

Electrodeposition in electrochemical cells is one of the leading causes of its performance deterioration. The prediction of electrodeposition growth demands a good understanding of the complex physics involved, which can lead to the fabrication of a probabilistic mathematical model. As an alternative, a convolutional long shortterm memory architecture-based image analysis approach is presented herein. This technique can predict the electrodeposition growth of the electrolytes, without prior detailed knowledge of the system. The captured images of the electrodeposition from the experiments are used to train and test the model. A comparison between the expected output image and predicted image on a pixel level, percentage mean squared error, absolute percentage error, and pattern density of the electrodeposit are investigated to assess the model accuracy. The randomness of the electrodeposition growth is outlined by investigating the fractal dimension and the interfacial length of the electrodeposits. The trained model predictions show a significant promise between all the experimentally obtained relevant parameters with the predicted one. It is expected that this deep learning-based approach for predicting random electrodeposition growth will be of immense help for designing and optimizing the relevant experimental scheme in near future without performing multiple experiments.

关键词： Electrodeposition Electrochemical cell Deep learning Data-driven modelling convolutional long short-term memory

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Use of Local Region Maps on convolutional LSTM for Single-Image HDR Reconstruction

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Computers, Materials & Continua 2022年第6期71卷 4555-4572页

作者： Seungwook Oh GyeongIk Shin Hyunki Hong College of Software Chung-Ang UniversityHeukseok-ro 84Dongjak-kuSeoul06973Korea

Low dynamic range(LDR)images captured by consumer cameras have a limited luminance *** the conventional method for generating high dynamic range(HDR)images involves merging multiple-exposure LDR images of the same scene(assuming a stationary scene),we introduce a learning-based model for single-image HDR *** input LDR image is sequentially segmented into the local region maps based on the cumulative histogram of the input brightness *** the local region maps,SParam-Net estimates the parameters of an inverse tone mapping function to generate a pseudo-HDR *** process the segmented region maps as the input sequences on long short-term ***,a fast super-resolution convolutional neural network is used for HDR image *** proposed method was trained and tested on datasets including HDR-Real,LDR-HDR-pair,and *** experimental results revealed that HDR images can be generated more reliably than using contemporary end-to-end approaches.

关键词： Low dynamic range high dynamic range deep learning convolutional long short-term memory inverse tone mapping function

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