Suchergebnisse - sparse convolutional autoencoder (SCAE)

Sparse convolutional autoencoder‐based fault location for drive circuits in nuclear reactors von Yang, Cheng, Yuan, Yannan, Wang, Fu, Li, Jueying, Li, Ang, Min, Yuan, Zhang, Qiang

“… Drive circuit is a critical part of instrumentation and control systems in nuclear reactors, and its performance directly influences the operation of nuclear …”
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Investigating Beta-Variational Convolutional Autoencoders for the Unsupervised Classification of Chest Pneumonia von Akila, Serag Mohamed, Imanov, Elbrus, Almezhghwi, Khaled

“… The world’s population is increasing and so is the challenge on existing healthcare infrastructure to cope with the growing demand in medical diagnosis and …”
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SCAE—Stacked Convolutional Autoencoder for Fault Diagnosis of a Hydraulic Piston Pump with Limited Data Samples von Eraliev, Oybek, Lee, Kwang-Hee, Lee, Chul-Hee

“… In this study, we propose a novel DL model based on a stacked convolutional autoencoder (SCAE) to address the challenge of limited data …”
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A radar emitter structural identification method for complex conditions based on compressed sensing and an autoencoder von Song, Yu, Zhang, Wei, Yang, Yaozu, Zhang, Xin, Jiang, Yilin

“… A stacked convolutional autoencoder (SCAE) network with strong representation learning capability, along with a loss function that integrates time-domain and sparse-domain constraints (TCS …”
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A radar emitter structural identification method for complex conditions based on compressed sensing and an autoencoder: A radar emitter structural identification method for complex conditions based von Song, Yu, Zhang, Wei, Yang, Yaozu, Zhang, Xin, Jiang, Yilin

“… A stacked convolutional autoencoder (SCAE) network with strong representation learning capability, along with a loss function that integrates time-domain and sparse-domain constraints (TCS …”
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Face Recognition Based on Stacked Convolutional Autoencoder and Sparse Representation von Chang, Liping, Yang, Jianjun, Li, Sheng, Xu, Hong, Liu, Kai, Huang, Chaogeng

“… To solve this problem, a stacked convolutional autoencoder (SCAE) based on deep learning theory is used to extract deeper features …”
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