Search Results - sparse autoencoder network ((sae OR sage)-net)~

Multisensor Feature Fusion for Bearing Fault Diagnosis Using Sparse Autoencoder and Deep Belief Network by Chen, Zhuyun, Li, Weihua

“… First, time-domain and frequency-domain features are extracted from the different sensor signals, and then these features are input into multiple two-layer sparse autoencoder (SAE…”
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Deep Transfer Learning Based on Sparse Autoencoder for Remaining Useful Life Prediction of Tool in Manufacturing by Sun, Chuang, Ma, Meng, Zhao, Zhibin, Tian, Shaohua, Yan, Ruqiang, Chen, Xuefeng

“… In this paper, a deep transfer learning (DTL) network based on sparse autoencoder (SAE) is presented. In the DTL method, three transfer strategies, that is, weight transfer…”
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A Novel Sparse Echo Autoencoder Network for Data-Driven Fault Diagnosis of Delta 3-D Printers by Long, Jianyu, Sun, Zhenzhong, Li, Chuan, Hong, Ying, Bai, Yun, Zhang, Shaohui

“… In this paper, a sparse echo autoencoder network (SEAEN) is proposed for the fault diagnosis of delta 3-D printers using attitude data…”
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Interpretable unsupervised neural network structure for data clustering via differentiable reconstruction of ONMF and sparse autoencoder by Gai, Yongwei, Liu, Jinglei

“…) into a clustering layer, which is directly incorporated into the OSINN neural network model and enhanced by a Sparse Autoencoder (SAE…”
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Time Series Prediction Using Sparse Autoencoder and High-Order Fuzzy Cognitive Maps by Wu, Kai, Liu, Jing, Liu, Penghui, Yang, Shanchao

“…The problem of time series prediction based on fuzzy cognitive maps (FCMs) is unresolved. Although many methods have been proposed to cope with this issue, the…”
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EEG-Based Emotion Classification Using a Deep Neural Network and Sparse Autoencoder by Liu, Junxiu, Wu, Guopei, Luo, Yuling, Qiu, Senhui, Yang, Su, Li, Wei, Bi, Yifei

“…), Sparse Autoencoder (SAE), and Deep Neural Network (DNN) together. In the proposed network, the features extracted by the CNN are first sent to SAE for encoding and decoding…”
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NMF-SAE: An Interpretable Sparse Autoencoder for Hyperspectral Unmixing by Xiong, Fengchao, Zhou, Jun, Ye, Minchao, Lu, Jianfeng, Qian, Yuntao

“…) inspired sparse autoencoder (NMF-SAE) for hyperspectral unmixing. NMF-SAE consists of an encoder and a decoder, both of which are constructed by unrolling the iterative…”
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A deep learning algorithm using a fully connected sparse autoencoder neural network for landslide susceptibility prediction by Huang, Faming, Zhang, Jing, Zhou, Chuangbing, Wang, Yuhao, Huang, Jinsong, Zhu, Li

“… In this paper, a novel deep learning–based algorithm, the fully connected spare autoencoder (FC-SAE), is proposed for LSP…”
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PEMFC Residual Life Prediction Using Sparse Autoencoder-Based Deep Neural Network by Liu, Jiawei, Li, Qi, Han, Ying, Zhang, Guorui, Meng, Xiang, Yu, Jiaxi, Chen, Weirong

“…) under dynamic operating conditions, this article proposes a PEMFC RUL forecast technique based on the sparse autoencoder (SAE…”
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SV-SAE: Layer-Wise Pruning for Autoencoder Based on Link Contributions by Rheey, Joohong, Park, Hyunggon

“… The resulting pruned model is referred to as a Shapley Value-based Sparse AutoEncoder (SV-SAE). Using cooperative game theory, the proposed algorithm models the autoencoder…”
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A hybrid Intrusion Detection System based on Sparse autoencoder and Deep Neural Network by Narayana Rao, K., Venkata Rao, K., P.V.G.D., Prasad Reddy

“… In the first stage, the unsupervised Sparse autoencoder (SAE) with smoothed l1 regularization…”
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Static video summarization using multi-CNN with sparse autoencoder and random forest classifier by Nair, Madhu S., Mohan, Jesna

“…). The features are extracted using four pre-trained models of CNN. These vectors are fed to Sparse Autoencoder, which outputs a combined representation of the input feature vectors…”
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Denoising magnetic resonance spectroscopy (MRS) data using stacked autoencoder for improving signal‐to‐noise ratio and speed of MRS by Wang, Jing, Ji, Bing, Lei, Yang, Liu, Tian, Mao, Hui, Yang, Xiaofeng

“…Background While magnetic resonance imaging (MRI) provides high resolution anatomical images with sharp soft tissue contrast, magnetic resonance spectroscopy…”
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A Stacked Autoencoder With Sparse Bayesian Regression for End-Point Prediction Problems in Steelmaking Process by Liu, Chang, Tang, Lixin, Liu, Jiyin

“… Considering the importance of data representation in modeling, the original data are inputted to a stacked autoencoder (SAE…”
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Building feature space of extreme learning machine with sparse denoising stacked-autoencoder by Cao, Le-le, Huang, Wen-bing, Sun, Fu-chun

“… Deep learning algorithms such as stacked autoencoder (SAE) and deep belief network (DBN) are built on learning several levels of representation of the input…”
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Stacked Sparse Autoencoder-Based Deep Network for Fault Diagnosis of Rotating Machinery by Qi, Yumei, Shen, Changqing, Wang, Dong, Shi, Juanjuan, Jiang, Xingxing, Zhu, Zhongkui

“… Thus, a stacked sparse autoencoder (SAE)-based machine fault diagnosis method is proposed in this paper…”
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Open-circuit fault diagnosis of power rectifier using sparse autoencoder based deep neural network by Xu, Lin, Cao, Maoyong, Song, Baoye, Zhang, Jiansheng, Liu, Yurong, Alsaadi, Fuad E.

“…This paper is concerned with the open-circuit fault diagnosis of phase-controlled three-phase full-bridge rectifier by using a sparse autoencoder-based deep neural network (SAE-based DNN…”
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An Explainable AI Framework Integrating Variational Sparse Autoencoder and Random Forest for EEG-Based Epilepsy Detection by Mishra, Pratiti, Das, Himansu

“… Therefore, this research proposes a novel artificial neural network architecture called the Variational Sparse Autoencoder (VSAE…”
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ASD-SAENet: A Sparse Autoencoder, and Deep-Neural Network Model for Detecting Autism Spectrum Disorder (ASD) Using fMRI Data by Almuqhim, Fahad, Saeed, Fahad

“… We designed and implemented a sparse autoencoder (SAE) which results in optimized extraction of features…”
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A Comparative Study of Lightweight, Sparse Autoencoder-Based Classifiers for Edge Network Devices: An Efficiency Analysis of Feed-Forward and Deep Neural Networks by Jo, Mi Young, Kim, Hyun Jung

“… Thirteen packet- and flow-level features extracted from the CIC-IDS2017 dataset were compressed into 4-dimensional latent vectors using a Sparse Autoencoder (SAE…”
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