Search Results - stacked denoising autoencoder((sade OR sae))

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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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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Roller bearing fault diagnosis using stacked denoising autoencoder in deep learning and Gath–Geva clustering algorithm without principal component analysis and data label by Xu, Fan, Tse, Wai tai Peter, Tse, Yiu Lun

“…Most deep learning models such as stacked autoencoder (SAE) and stacked denoising autoencoder (SDAE…”
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A sparse stacked denoising autoencoder with optimized transfer learning applied to the fault diagnosis of rolling bearings by Sun, Meidi, Wang, Hui, Liu, Ping, Huang, Shoudao, Fan, Peng

“… As an unsupervised deep learning algorithm, a stacked autoencoder (SAE) can relieve the pressure of labelling data…”
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Object-Based Land-Cover Supervised Classification for Very-High-Resolution UAV Images Using Stacked Denoising Autoencoders by Zhang, Xiaodong, Chen, Guanzhou, Wang, Wenbo, Wang, Qing, Dai, Fan

“… Second, we extract the spectral, spatial, and texture features for each object. Then we put all features into stacked autoencoders (SAE…”
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Toward Robust Fault Identification of Complex Industrial Processes Using Stacked Sparse-Denoising Autoencoder With Softmax Classifier by Liu, Jinping, Xu, Longcheng, Xie, Yongfang, Ma, Tianyu, Wang, Jie, Tang, Zhaohui, Gui, Weihua, Yin, Huazhan, Jahanshahi, Hadi

“…This article proposes a robust end-to-end deep learning-induced fault recognition scheme by stacking multiple sparse-denoising autoencoders with a Softmax classifier, called stacked spare-denoising autoencoder (SSDAE…”
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Life prediction of lithium-ion batteries based on stacked denoising autoencoders by Xu, Fan, Yang, Fangfang, Fei, Zicheng, Huang, Zhelin, Tsui, Kwok-Leung

“… In this study, a deep learning-based stacked denoising autoencoder (SDAE) method is proposed to directly predict battery life by extracting various battery features…”
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Stacked Fisher autoencoder for SAR change detection by Liu, Ganchao, Li, Lingling, Jiao, Licheng, Dong, Yongsheng, Li, Xuelong

“… Stacked autoencoder is effective in image denoising and classification when it is used for synthetic aperture radar (SAR) change detection…”
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Fault prediction of combine harvesters based on stacked denoising autoencoders by Qiu, Zhaomei, Shi, Gaoxiang, Zhao, Bo, Jin, Xin, Zhou, Liming, Ma, Tengfei

“… In this study, a combine harvester fault prediction method based on a combination of stacked denoising autoencoders (SDAE…”
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Automatic Diagnosis of Microgrid Networks’ Power Device Faults Based on Stacked Denoising Autoencoders and Adaptive Affinity Propagation Clustering by Fan, Bo, Zhang, Xiaodi, Shu, Xin, Xu, Fan

“…This paper presents a model based on stacked denoising autoencoders (SDAEs) in deep learning and adaptive affinity propagation (adAP…”
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A deep learning framework for financial time series using stacked autoencoders and long-short term memory by Bao, Wei, Yue, Jun, Rao, Yulei

“… This study presents a novel deep learning framework where wavelet transforms (WT), stacked autoencoders (SAEs…”
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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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Data-Driven Bearing Fault Diagnosis of Microgrid Network Power Device Based on a Stacked Denoising Autoencoder in Deep Learning and Clustering by Fast Search without Data Labels by Zhang, Xiaodi, Li, Xin, Shu, Xin, Xu, Fan

“… In this paper, we use the stacked denoising autoencoder (SDAE) model in deep learning to construct HI directly from the microgrid power equipment of raw signals in bearings…”
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Denoised Bottleneck Features From Deep Autoencoders for Telephone Conversation Analysis by Janod, Killian, Morchid, Mohamed, Dufour, Richard, Linares, Georges, De Mori, Renato

“… Recently, denoisng autoencoders (DAE) and stacked autoencoders (SAE) have been proposed with interesting results for acoustic feature denoising tasks…”
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A Deep Learning Model to Predict the ncRNA-Protein Interactions Based on Sequences Information Only by Sewailem, Maha FM, Arif, Muhammad, Alam, Tanvir

“…Noncoding RNAs (ncRNAs) play significant roles in multiple fundamental biological processes, in particular, ncRNAs interactions provide valuable insights into…”
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Recognition of Cognitive Task Load levels using single channel EEG and Stacked Denoising Autoencoder by Yin, Zhong, Zhang, Jianhua

“… is particularly challenging as EEG is characterized by individual dependency and nonstationarity. In this paper, a deep learning model designed by Stacked Denoising AutoEncoder…”
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Manifold regularized stacked autoencoders-based feature learning for fault detection in industrial processes by Yu, Jianbo, Zhang, Chengyi

“… Recently, deep neural networks (DNNs), i.e., stacked autoencoder (SAE) enjoys its popularization in process fault detection…”
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Face recognition algorithm based on stack denoising and self-encoding LBP by Lu, Yanjing, Khan, Mudassir, Ansari, Mohd Dilshad

“…) and stacked autoencoder (AE) is proposed. The advantage of LBP texture structure feature of the face image as the initial feature of sparse autoencoder (SAE…”
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Constructing a health indicator for bearing degradation assessment via an unsupervised and enhanced stacked autoencoder by Xu, Fan, Wang, Lei

“… To demonstrate our proposed method is better than other models, including the stacked autoencoder (SAE…”
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Stacked maximum independence autoencoders: A domain generalization approach for fault diagnosis under various working conditions by Pang, Shan

“…One of the major obstacles in rotating machinery fault diagnosis is the distribution discrepancy in feature space caused by the change of working conditions…”
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