Search Results - Multiscale convolutional autoencoder network

Multiscale Variational Autoencoder Aided Convolutional Neural Network for Pose Estimation of Tunneling Machine Using a Single Monocular Image by Wu, Hongzhuang, Liu, Songyong, Cheng, Cheng, Cao, Sheng, Cui, Yuming, Zhang, Deyi

“…With the rising demand of underground construction, intelligent tunneling techniques have been increasingly studied to improve the safety and efficiency of…”
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An unsupervised feature learning based health indicator construction method for performance assessment of machines by Guo, Liang, Yu, Yaoxiang, Duan, Andongzhe, Gao, Hongli, Zhang, Jiangquan

“…•A multi-scale CAE network is used to extract features from three scale levels.•Only the data collected under health conditions are used for network updating…”
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MTCR-AE: A Multiscale Temporal Convolutional Recurrent Autoencoder for unsupervised malicious network traffic detection by Ahmed, Mukhtar, Chen, Jinfu, Akpaku, Ernest, Sosu, Rexford Nii Ayitey

“… This paper introduces the Multiscale Temporal Convolutional Recurrent Autoencoder (MTCR-AE), an innovative framework designed to detect malicious network traffic by leveraging Multiscale Temporal Convolutional Networks…”
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Stressed Vegetation Identification Under Natural Gas Microleakage From Hyperspectral Images Using Stacked Autoencoder and Multiscale Three-Dimensional Convolutional Neural Network by Xiong, Kangni, Jiang, Jinbao, Ran, Weiwei, Li, Kangning, Pan, Yingyang, Wang, Xinda

“…) and multiscale three-dimensional convolutional neural network (MS3D CNN) was proposed for stress identification of grass, soybean, corn, and wheat…”
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IoT-based prediction model for aquaponic fish pond water quality using multiscale feature fusion with convolutional autoencoder and GRU networks by Sundararajan, Suma Christal Mary, Shankar, Yamini Bhavani, Selvam, Sinthia Panneer, Manogaran, Nalini, Seerangan, Koteeswaran, Natesan, Deepa, Selvarajan, Shitharth

“…The Internet of Things (IoT)-based smart solutions have been developed to predict water quality and they are becoming an increasingly important means of…”
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Multiscale CNN With Autoencoder Regularization Joint Contextual Attention Network for SAR Image Classification by Wu, Zitong, Hou, Biao, Jiao, Licheng

“… In this study, a new SAR classification algorithm known as the multiscale convolutional neural network with an autoencoder regularization joint contextual attention network (MCAR-CAN) is proposed…”
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An Unsupervised Approach to Power Transformer Early Fault Warning Based on PMCAEN and SVDD by Zhou, Yazhong, He, Yigang, Xing, Zhikai, Wang, Lei, Shao, Kaixuan, He, Liulu, Zhang, Chenran, Li, Wenyue

“… Specifically, a new unsupervised learning framework, parallel multiscale convolutional autoencoder network (PMCAEN…”
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Incipient Fault Detection Based on Multiscale Time Series Feature Extraction by Wang, Chengcheng, Sheng, Ke, Liu, Zhen, Wang, Jinjiang, Wang, Min

“…: 1) lack of fault data and 2) hidden abnormal features in time series. Therefore, an incipient fault detection method which is called autoencoder multiscale temporal convolutional network (AMTCN…”
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Knowledge Embedded Autoencoder Network for Harmonic Drive Fault Diagnosis Under Few-Shot Industrial Scenarios by Chen, Jiaxian, Wen, Kairu, Xia, Jingyan, Huang, Ruyi, Chen, Zhuyun, Li, Weihua

“…The development of Internet of Things technology provides abundant data resources for prognostics health management of industrial machinery, and data-driven…”
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Deep learning analysis of the myocardium in coronary CT angiography for identification of patients with functionally significant coronary artery stenosis by Zreik, Majd, Lessmann, Nikolas, van Hamersvelt, Robbert W., Wolterink, Jelmer M., Voskuil, Michiel, Viergever, Max A., Leiner, Tim, Išgum, Ivana

“…•Presence of functionally significant coronary stenosis is determined automatically.•Functional significance of the stenosis is determined by myocardium…”
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Multiscale Feature-Clustering-Based Fully Convolutional Autoencoder for Fast Accurate Visual Inspection of Texture Surface Defects by Yang, Hua, Chen, Yifan, Song, Kaiyou, Yin, Zhouping

“… or their time-consuming sliding-window strategy. In this paper, we present a novel unsupervised multiscale feature-clustering-based fully convolutional autoencoder (MS-FCAE…”
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Self-Supervised Graph Masked Autoencoders for Hyperspectral Image Classification by Hu, Zhenghao, Tu, Bing, Liu, Bo, He, Yan, Li, Jun, Plaza, Antonio

“…). It innovatively employs graph masked autoencoders to achieve self-supervised label-free feature extraction for the complete set of samples, utilizing a multiscale graph convolutional network encoder (MGCNE…”
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Multiresolution convolutional autoencoders by Liu, Yuying, Ponce, Colin, Brunton, Steven L., Kutz, J. Nathan

“…: (i) multigrid methods, (ii) convolutional autoencoders and (iii) transfer learning. The method provides an adaptive, hierarchical architecture that capitalizes on a progressive training approach for multiscale spatio-temporal data…”
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Anomaly detection for real-world electric vehicle charging data using a convolutional autoencoder with multiscale convolution, attention mechanism, and BiLSTM by Liu, Zhibin, Li, Lei, Ding, Xiaoyin, Wang, Xia, Liu, Zhiheng, Wang, Yawen, Hu, Changpeng

“… To improve the detection of anomalies in complex EV charging data, this study proposes a method based on an enhanced convolutional autoencoder (CoAE), MA-BiLSTM-MCoAE…”
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Multiscale variational autoencoder regressor for production prediction and energy saving of industrial processes by Han, Yongming, Wang, Yue, Chen, Zhiwei, Lu, Yi, Hu, Xuan, Chen, Liangchao, Geng, Zhiqiang

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A Spatiotemporal Fusion Autoencoder-Based Health Indicator Automatic Construction Method for Rotating Machinery Considering Vibration Signal Expression by Duan, Yong, Cao, Xiangang, Zhao, Jiangbin, Li, Man, Yang, Xin

“…) is developed by integrating multiscale convolution (MSCNN), convolutional long short-term memory network (ConvLSTM…”
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A Joint Multiscale Graph Attention and Classify-Driven Autoencoder Framework for Hyperspectral Unmixing by Cao, Feilong, Situ, Yujia, Ye, Hailiang

“…Deep learning has recently gained popularity in hyperspectral unmixing (HU) and typical methods involve convolutional neural network-based (CNN-based…”
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Spatial-Spectral Hierarchical Multiscale Transformer-Based Masked Autoencoder for Hyperspectral Image Classification by Liu, Haipeng, Ye, Zhen, Hu, Wen-Shuai, Cao, Zhan, Li, Wei

“… As such, based on the self-supervised learning, this article proposes a spatial-spectral hierarchical multiscale transformer-based masked autoencoder (SSHMT-MAE…”
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Multiscale Convolutional Mask Network for Hyperspectral Unmixing by Xu, Mingming, Xu, Jin, Liu, Shanwei, Sheng, Hui, Yang, Zhiru

“… Therefore, inspired by the effectiveness of mask modeling, we propose a multiscale convolutional mask network (MsCM-Net…”
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HHGNN: Hyperbolic Hypergraph Convolutional Neural Network based on variational autoencoder by Mei, Zhangyu, Bi, Xiao, Wen, Yating, Kong, Xianchun, Wu, Hao

“… To address this gap and leverage multilevel aggregation for capturing high-order hidden information in local representations, we propose the Hyperbolic Hypergraph Convolutional Neural Network (HHGNN…”
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