Search Results - Multi-scale sparse autoencoder

An industrial process monitoring method based on fusion of multi-scale sparse autoencoder and dual-branch slow feature architecture by Ling, Guobi, Wang, Zhiwen, Wang, Jieying, Shi, Yaoke, Li, Long, Zeng, Jingxiao

“…•MSAE-DSFA merges multi-scale sparse AEs and dual slow feature for monitoring.•Tackles info redundancy, nonlinearity and slow faults in industrial processes…”
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PolSAR image classification based on multi-scale stacked sparse autoencoder by Zhang, Lu, Jiao, Licheng, Ma, Wenping, Duan, Yiping, Zhang, Dan

“… Hence, this paper proposes a multi-scale feature extraction method based on stacked sparse autoencoder (SSAE…”
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An Intelligent Fault Diagnosis Method of Multi-Scale Deep Feature Fusion Based on Information Entropy by Shang, Zhiwu, Li, Wanxiang, Gao, Maosheng, Liu, Xia, Yu, Yan

“… First, a normal autoencoder, denoising autoencoder, sparse autoencoder, and contractive autoencoder are used in parallel to construct a multi-scale deep neural network feature extraction structure…”
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Anomaly Detection In Retinal Images Using Multi-Scale Deep Feature Sparse Coding by Das, Sourya Dipta, Dutta, Saikat, Shah, Nisarg A., Mahapatra, Dwarikanath, Ge, Zongyuan

“… We have proposed a simple, memory efficient, easy to train method which followed a multi-step training technique that incorporated autoencoder training and Multi-Scale Deep Feature Sparse Coding (MDFSC…”
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SSAE‐MLP: Stacked sparse autoencoders‐based multi‐layer perceptron for main bearing temperature prediction of large‐scale wind turbines by Xiao, Xiaocong, Liu, Jianxun, Liu, Deshun, Tang, Yufei, Dai, Juchuan, Zhang, Fan

“… To achieve the goal, this paper proposes a novel deep learning approach named stacked sparse autoencoder multi‐layer perceptron (SSAE‐MLP…”
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Enhancing the robustness of the 1-D CNN model through NIRS data augmentation based on sparse autoencoder and CARS feature selection for mango DMC determination by Yang, Dongfeng, Hu, Jun

“… In this paper, we propose a spectral data augmentation method based on the sparse autoencoder and establish a one-dimensional convolutional model to predict mango DMC…”
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Rethinking link prediction: A multi-scale graph masked autoencoder by Zhang, Guotai, Zuo, Enguang, Yan, Ziwei, Chen, Chen, Chen, Cheng, Lv, Xiaoyi

“… Therefore, we revisit these two approaches from a novel perspective and propose a multi-scale graph masked autoencoder (MS-GMAE…”
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Multiscale Mesh Deformation Component Analysis With Attention-Based Autoencoders by Yang, Jie, Gao, Lin, Tan, Qingyang, Huang, Yi-Hua, Xia, Shihong, Lai, Yu-Kun

“… The attention mechanism is designed to learn to softly weight multi-scale deformation components in active deformation regions, and the stacked attention-based autoencoder is learned to represent…”
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Multi source heterogeneous data diagnosis method of rotating machinery based on parameter collaborative optimization of multi-scale convolutional autoencoder by Yang, Xiaoli

“… data diagnosis method based on parameter collaborative optimization multi-scale convolutional autoencoder (MSCAE) is proposed…”
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An Intelligent Fault Diagnosis Method of Multi-Scale Deep Feature Fusion Based on Information Entropy by Zhiwu Shang, Wanxiang Li, Maosheng Gao, Xia Liu, Yan Yu

“… entropy.First,a normal autoencoder,denoising autoencoder,sparse autoencoder,and contractive autoencoder are used in parallel to construct a multi-scale deep neural network feature extrac-tion…”
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Hyperspectral anomaly detection with semisupervised variational background inference and multi-scale masked convolutions by Li, Zhongwei, Wei, Jie, Wang, Leiquan, Yang, Yajie, Wang, Yuewen

“… To address these problems, we propose a novel hyperspectral anomaly detection method with semisupervised variational background inference and a multi-scale masked convolutions network, termed SVBMCN…”
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Spectral-spatial hyperspectral image classification combining multi-scale bi-exponential edge-preserving filtering and SUSAN edge detector by Wan, Xiaoqing, Zhao, Chunhui

“… First, the multi-scale bi-exponential edge-preserving smoother (BEEPS) is introduced into HSI for the first time…”
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Learning a compact latent representation of the Bag-of-Parts model by Xiaozhi Chen, Huimin Ma

“… We address this problem by employing spectral clustering and a multi-column coding scheme. The BoP model is viewed as a multi-scale convolutional model and additional…”
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Multi‐scale feature fusion pyramid attention network for single image dehazing by Liu, Jianlei, Liu, Peng, Zhang, Yuanke

“… SDF considers multi‐scale as well as channel‐level fusion to provide feature information under different receptive fields while also highlighting important…”
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Deep Learning in Image Processing and Pattern Recognition

“…This Reprint aims to provide readers with an extensive insight into the latest developments in image processing technology. As images are the main method by…”
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Multi-stream pyramid collaborative network for spectral unmixing by Wang, Jie, Ni, Mengying, Wang, Zhixiang, Yan, Yu, Cheng, Xiang, Xu, Jindong

“… Therefore, we propose a collaborative unmixing network with a multi-scale pyramid structure to capture both global and local features simultaneously…”
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Fault Diagnosis of Rolling Bearing Based on Multiscale Intrinsic Mode Function Permutation Entropy and a Stacked Sparse Denoising Autoencoder by Dai, Juying, Tang, Jian, Shao, Faming, Huang, Shuzhan, Wang, Yangyang

“… To realize representative features mining and automatic recognition of bearing health condition, a diagnostic model of stacked sparse denoising autoencoder (SSDAE…”
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A Multi-Scale Feature Extraction Framework for Monitoring in Municipal Wastewater Treatment Plants by Zhao, Yaqian, Han, Honggui, Wu, Xiaolong

“… To address this issue, we propose MSFEM (Multi-Scale Feature Extraction Method), a framework that integrates temporal alignment, multi-scale self-attention, and sparse representation learning…”
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A Multivariate Time Series Anomaly Detection Model Based on Spatio-Temporal Dual Features by Wang, Fangwei, Yan, Man, Li, Qingru, Wang, Changguang

“… Specifically, a deep autoencoder is employed to capture the spatial features of multivariate time series data, while a multi-scale sparse Transformer network is used to extract the temporal features…”
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An automatic and integrated self-diagnosing system for the silting disease of drainage pipelines based on SSAE-TSNE and MS-LSTM by Di, Danyang, Wang, Dianchang, Fang, Hongyuan, He, Qiang, Zhou, Lifen, Chen, Xianming, Sun, Bin, Zhang, Jinping

“…•Multi-scale sampling improves the accuracy and robustness of siltation diagnosis. The regular detection and diagnosis mechanism for the silting disease of drainage pipelines (SDP…”
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