Search Results - "convolution autoencoder"

Deep Self-Supervised Graph Attention Convolution Autoencoder for Networks Clustering by Chen, Chao, Lu, Hu, Hong, Haotian, Wang, Hai, Wan, Shaohua

“… To solve this problem, we propose a Deep Self-Supervised Attention Convolution Autoencoder Graph Clustering (DSAGC…”
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Fault Diagnosis in Centrifugal Pumps: A Dual-Scalogram Approach with Convolution Autoencoder and Artificial Neural Network by Zaman, Wasim, Ahmad, Zahoor, Kim, Jong-Myon

“…This paper proposes a new fault diagnosis method for centrifugal pumps by combining signal processing with deep learning techniques. Centrifugal pumps…”
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A hybrid deep learning-based fruit classification using attention model and convolution autoencoder by Xue, Gang, Liu, Shifeng, Ma, Yicao

“… chain, factories, supermarkets, and other fields. In this paper, we develop a hybrid deep learning-based fruit image classification framework, named attention-based densely connected convolutional networks with convolution autoencoder (CAE-ADN…”
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DeepSORT with siamese convolution autoencoder embedded for honey peach young fruit multiple object tracking by Zhang, Tian, Zhao, Dongfang, Chen, Yesheng, Zhang, Hongli, Liu, Shulin

“…•A staged data preprocessing strategy for honey peach young fruit is designed.•The SCAE is proposed to ensure the discriminability of the extracted…”
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Self-Tuning Transfer Dynamic Convolution Autoencoder for Quality Prediction of Multimode Processes With Shifts by Yang, Chao, Liu, Qiang, Wang, Chen, Ding, Jinliang, Cheung, Yiu-ming

“… First, in order to capture domain-invariant spatiotemporal (DIST) features, a transfer dynamic convolution autoencoder (TDCAE…”
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STEAMCODER: Spatial and Temporal Adaptive Dynamic Convolution Autoencoder for Anomaly Detection by Xu, Peng, Gan, Honglu, Fu, Hua, Zhang, Zhongbao

“… Hence, an adaptive algorithm is required to address these problems. We design a novel algorithm called Spatial-TEmporal Adaptive dynaMic Convolution autoencODER for Anomaly Detection (STEAMCODER), specifically…”
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Convolution Autoencoder based Classification Model for Content Based Image Retrieval by Rai, Rishi, Sisodia, Dilip Singh

“…The Content Based Image Retrieval (CBIR) has gained significant importance due to surge of online images and its diverse applications. The CBIR system gets an…”
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Fault Feature of Gearbox Vibration Signals Based on Morphological Filter Dynamic Convolution Autoencoder by Yu, Jianbo, Huang, Jiahuan, Liu, Changhui, Xia, Beixin

“…Vibration signals are widely used in fault diagnosis of rotating machinery. Although deep neural networks (DNNs) have been widely used in feature learning of…”
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Temporal denoising and deep feature learning for enhanced defect detection in thermography using stacked denoising convolution autoencoder by Yerneni, Naga Prasanthi, Ghali, V.S., Vesala, G.T., Wang, Fei, Mulaveesala, Ravibabu

“…•Temporal denoising of thermal profiles along with deep feature learning for enhanced defect detection in FMTWI is highlighted through a stacked denoising convolution autoencoder…”
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Fabric Defect Detection Using a One-class Classification Based on Depthwise Separable Convolution Autoencoder by Chen, Chun, Deng, Xiaoyan, Yu, Zhuliang, Wu, Zhengtao

“… This paper proposes a method applying depthwise separable convolution autoencoder on dimensionality reduction and one-class classifier support vector data description (SVDD…”
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Industrial Robot Vibration Anomaly Detection Based on Sliding Window One-Dimensional Convolution Autoencoder by Zhong, ZhiDan, Zhao, Yao, Yang, AoYu, Zhang, HaoBo, Qiao, DongHao, Zhang, ZhiHui

“…Model-based methods can be used to detect anomalies in industrial robots, but they require a high level of expertise and are therefore difficult to implement…”
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Parametric Study of Convolution Autoencoder for Reduced-Order Modeling of Turbulent Flow by Beiki, Alireza, Kamali, Reza

“…In this paper, we analyze the capabilities of several multiscale convolutional autoencoder architectures for reduced-order modeling of two-dimensional unsteady…”
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Identifying Functional Brain Networks under Naturalistic Paradigm via A Three-Dimensional Spatial Attention Convolution Autoencoder by Ren, Yudan, Yin, Song, Liu, Zhengyang, Wang, Kexin, Le, Mingnan, Zhang, Wei, Li, Xiao

“…Functional Magnetic Resonance Imaging under the naturalistic paradigm (NfMRI) has great advantages in triggering complex and interactive functional brain…”
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A Truth Inference Algorithm Using Bidirectional Convolution Autoencoder for Crowdsourcing Image Segmentation by Li, Yueyang, Dong, Zhaoan, Li, Guangshun, Wang, Sifeng, Wang, Xiaofan, Liu, Zhibin, Li, Zhenqiang, Wang, Boyong

“…In the paper, we propose a truth inference algorithm based on bidirectional convolutional autoencoder to capture and utilize the internal structure information…”
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Dynamic mechanical response prediction model of honeycomb structure based on machine learning method and finite element method by Shen, Xingyu, Hu, Qianran, Zhu, Difeng, Qi, Shaobo, Huang, Chunyang, Yuan, Mengqi, Qian, Xinming, Guo, Ran

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Direct Multitype Cardiac Indices Estimation via Joint Representation and Regression Learning by Wufeng Xue, Islam, Ali, Bhaduri, Mousumi, Shuo Li

“…Cardiac indices estimation is of great importance during identification and diagnosis of cardiac disease in clinical routine. However, estimation of multitype…”
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Network Intrusion Detection System Based on Separable Convolution AutoEncoder with Long Short-Term Memory by Zhang, Fulin

“… Therefore, this research proposes Separable Convolution AutoEncoder-Long Short-Term Memory (SCAE-LSTM…”
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Online Adaptive Prediction of Human Motion Intention Based on sEMG by Ding, Zhen, Yang, Chifu, Wang, Zhipeng, Yin, Xunfeng, Jiang, Feng

“… The feature extraction utilizes the convolution autoencoder network combined with muscle synergy characteristics to get the high-compression sEMG feature to aid motion prediction…”
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Transient voltage stability assessment based on focal loss convolution autoencoder by Zhu, L., Wang, Y., Shang, C., Zheng, Z.

“… assessment method based on focal loss convolution autoencoder (FL-CAE) is proposed. Firstly, a two-dimensional convolutional autoencoder (CAE…”
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PMSM fault detection using unsupervised learning methods based on conditional convolution autoencoder by Kozovsky, Matus, Buchta, Ludek, Blaha, Petr

“…The challenges of fault detection and condition monitoring in powertrain systems have become increasingly prominent, particularly with the widespread adoption…”
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