Výsledky vyhledávání - residual convolutional autoencoder

One-Dimensional Residual Convolutional Autoencoder Based Feature Learning for Gearbox Fault Diagnosis Autor Yu, Jianbo, Zhou, Xingkang

“… In this article, a new DNN, one-dimensional residual convolutional autoencoder (1-DRCAE), is proposed for learning features from vibration signals directly in an unsupervised-learning way…”
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Pulsar candidate selection with residual convolutional autoencoder Autor Yin, Qian, Li, Jiajie, Zheng, Xin, Li, Yefan, Cui, Hanshuai, Bao, Zelun

“… In this paper, we design a residual convolutional autoencoder (RCAE) based on the structure of the autoencoder, and combine with logistic regression (LR…”
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Integration of Adversarial Autoencoders With Residual Dense Convolutional Networks for Estimation of Non‐Gaussian Hydraulic Conductivities Autor Mo, Shaoxing, Zabaras, Nicholas, Shi, Xiaoqing, Wu, Jichun

“… In this study, we develop a convolutional adversarial autoencoder (CAAE) to parameterize non‐Gaussian conductivity fields with heterogeneous conductivity within each facies using a low…”
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Salient Object Detection in Stereoscopic 3D Images Using a Deep Convolutional Residual Autoencoder Autor Zhou, Wujie, Wu, Junwei, Lei, Jingsheng, Hwang, Jenq-Neng, Yu, Lu

“… Hence, we propose a novel Deep Convolutional Residual Autoencoder (DCRA) for end-to-end salient object detection in stereoscopic 3D images…”
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Semi-supervised underwater acoustic source localization based on residual convolutional autoencoder Autor Jin, Pian, Wang, Biao, Li, Lebo, Chao, Peng, Xie, Fangtong

“…Passive localization of underwater targets was a thorny problem in underwater acoustics. For traditional model-driven passive localization methods, the main…”
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Attention model based on one-dimensional residual convolutional autoencoder and GRU for knowledge tracing Autor Ai, Lingmei, Zhang, Xiaoying

“… Therefore, an attention knowledge tracing model based on one-dimensional residual convolutional autoencoder and GRU is proposed in this paper…”
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Traffic sign classification with deformable convolution based on denoising residual convolutional autoencoder region localization Autor Pan, Hao, Guo, Qianlu, Yuan, Decheng, Pan, Duotao, Li, Dong, Yu, Qianxin

“… autoencoder for important region localization and deformable convolution (DRCAE-DCN). The denoising residual convolutional autoencoder is used to extract feature…”
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A Residual Convolutional Autoencoder-Based Structural Damage Detection Approach for Deep-Sea Mining Riser Considering Data Fusion Autor Jiang, Yufeng, Zheng, Zepeng, Liu, Yu, Wang, Shuqing, Liu, Yuchi, Yang, Zeyun, Yang, Yuan

“… To address this challenge, a novel approach based on a residual convolutional auto-encoder (RCAE…”
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Graph Laplacian-Improved Convolutional Residual Autoencoder for Unsupervised Human Action and Emotion Recognition Autor Paoletti, Giancarlo, Beyan, Cigdem, Del Bue, Alessio

“… To lessen these shortcomings, this paper proposes a convolutional residual autoencoder…”
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BORCAE: Bayesian Optimized Residual Convolutional Autoencoder for Efficient Feedback Compression in RIS-Assisted Time-Varying IoT Networks Autor Bhardwaj, Vikash Kumar, Singh, Abhishek, Sharma, Shourya, Shukla, Mahendra K., Pandey, Om Jee

“… In this work, we propose Bayesian Optimized Residual Convolutional AutoEncoder (BORCAE), a lightweight and noise-resilient feedback compression framework based on a 1D Convolutional Autoencoder with residual connections…”
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Residual convolutional autoencoder combined with a non-negative matrix factorization to estimate fetal heart rate Autor Lafaye de Micheaux, Hugo, Resendiz, Mariel, Rivet, Bertrand, Fontecave-Jallon, Julie

“… In this context, we propose a deep residual convolutional autoencoder network trained on synthetic aECG simulations followed by a transfer learning phase on real aECG recordings to extract the cleanest fECG…”
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Residual attention convolutional autoencoder for feature learning and fault detection in nonlinear industrial processes Autor Liu, Xing, Yu, Jianbo, Ye, Lyujiangnan

“… A new deep neural network, residual attention convolutional autoencoder (RACAE) is proposed for process monitoring…”
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A Residual Learning-Aided Convolutional Autoencoder for SCMA Autor Jiang, Fang, Chang, Da-Wei, Ma, Song, Hu, Yan-Jun, Xu, Yao-Hua

“…) scenario, we design a new end-to-end autoencoder combining convolutional neural networks (CNNs…”
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Anomaly detection using convolutional autoencoder with residual gated recurrent unit and weak supervision for photovoltaic thermal heat pump system Autor John, Lukudu Samuel, Yoon, Sungmin, Li, Jiteng, Wang, Peng

“…Utilizing Convolutional Autoencoders with Residual Bi-Directional Gated Recurrent Unit bottleneck offers an advanced approach for detecting anomalies in heat pumps…”
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Radio Galaxy Morphology Generation using Residual Convolutional Autoencoder and Gaussian Mixture Models Autor Ma, Zhixian, Zhu, Jie, Li, Weitian, Xu, Haiguang

“…) using residual convolutional autoencoder (RCAE) and Gaussian mixture models (GMMs). The encoder and decoder subnets in the RCAE are symmetric aside a fully-connected…”
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Data-driven seismic prestack velocity inversion via combining residual network with convolutional autoencoder Autor Liu, Guanchao, Zhang, Lei, Wang, Qingzhen, Xu, Jianhua

“…•A residual convolutional autoencoder is proposed for 2D to 1D velocity reduction…”
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Reconstruction of SMAP Soil Moisture Data Based on Residual Autoencoder Network with Convolutional Feature Extraction Autor Liu, Yaojie, Fan, Haoyu, Jin, Yan, Zhu, Shaonan

“… This study presents a residual autoencoder model named TsSMNet, which combines multi-source remote sensing inputs with statistical features derived from SSM time series, including central tendency…”
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ERA-WGAT: Edge-enhanced residual autoencoder with a window-based graph attention convolutional network for low-dose CT denoising Autor Liu, Han, Liao, Peixi, Chen, Hu, Zhang, Yi

“… limited for non-local information extraction. This paper proposes ERA-WGAT, a residual autoencoder incorporating an edge enhancement module that performs convolution with eight types of learnable operators providing rich edge information…”
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Drug-target interaction prediction based on graph convolutional autoencoder with dynamic weighting residual GCN Autor Zeng, Ming, Wang, Min, Xie, Fuqiang, Ji, Zhiwei

“… of network’s representation capabilities. Results In this paper, we propose a graph convolutional autoencoder model, named DDGAE, for DTIs prediction…”
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Rice Leaf Disease Classification—A Comparative Approach Using Convolutional Neural Network (CNN), Cascading Autoencoder with Attention Residual U-Net (CAAR-U-Net), and MobileNet-V2 Architectures Autor Dutta, Monoronjon, Islam Sujan, Md Rashedul, Mojumdar, Mayen Uddin, Chakraborty, Narayan Ranjan, Marouf, Ahmed Al, Rokne, Jon G., Alhajj, Reda

“…) and the Peak Signal-to-Noise Ratio (PSNR). Following this, this work employed advanced neural network architectures for classification, including Cascading Autoencoder with Attention Residual U-Net (CAAR-U-Net…”
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