Search Results - stacked denoised autoencoder

Fault detection and recognition of multivariate process based on feature learning of one-dimensional convolutional neural network and stacked denoised autoencoder by Zhang, Chengyi, Yu, Jianbo, Wang, Shijin

“…) and stacked denoising auto-encoders (SDAE) to extract high level features from complex process signals…”
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Coupled fault diagnosis for centrifugal pumps through Boruta-Shap feature selection and rime-enhanced stacked denoised autoencoder by Hu, Kang, Sun, Hui, Fan, Wei, Si, Qiaorui, Wu, Yu, Yuan, Shouqi

“… from feature redundancy and poor generalization. An artificial intelligence (AI)-driven framework is proposed by integrating Boruta-Shap interpretable feature selection with a Rime-optimized Stacked Denoising Auto-Encoder (SDAE…”
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A Novel Method Based on Hybridization of Generative Adversarial Imputation Nets and SDAE-Kriging for RUL Prediction of Lithium-Ion Battery in Scenarios of Missing and Incomplete Data by Li, Wei, Li, Yongsheng, Wang, Ningbo, Garg, Akhil, Gao, Liang, Bose, Bibaswan, Shankhwar, Kalpana

“…) and Stacked denoised autoencoder with Kriging (SDAE-Kriging) for the prediction of RUL of LIBs in scenarios of missing and incomplete data…”
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基于改进堆叠降噪自编码器的配电网高阻接地故障检测方法 by 罗国敏, 杨雪凤, 尚博阳, 罗思敏, 和敬涵, 王小君

“…针对配电网高阻故障判定阈值选取难、噪声影响大和识别精度低等问题,提出了一种基于改进堆叠降噪自编码器的高阻接地故障检测方法,从特征提取及网络模型两个层面增强检测方法…”
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Automated Analysis of Sleep Study Parameters Using Signal Processing and Artificial Intelligence by Sohaib, Muhammad, Ghaffar, Ayesha, Shin, Jungpil, Hasan, Md Junayed, Suleman, Muhammad Taseer

“… In this research work, an empirical mode decomposition is used in combination with stacked autoencoders to conduct automatic sleep stage classification with reliable analytical performance…”
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Rolling Bearing Incipient Fault Detection via Optimized VMD Using Mode Mutual Information by Tan, Shuai, Wang, Aimin, Shi, Hongbo, Guo, Lei

“…The complete failure of the rolling bearing is a deterioration process from the incipient weak fault to the severe fault, thus it is important to alarm when…”
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Denoising Letter Images from Scanned Invoices Using Stacked Autoencoders by Ibrahim, Muhammad, Hanif, Muhammad, Ahmad, Shabir, Jamil, Faisal, Sehar, Tayyaba, Lee, YunJung, Kim, DoHyeun

“… This affects the OCR (optical character recognition) detection accuracy. In this paper, letter data obtained from images of invoices are denoised using a modified autoencoder based deep learning method…”
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Fault Diagnosis for Hydraulic Servo System: A Stacked Denoising Autoencoder Method based on Self-Learning of Robustness Features by Wang, Zhenya, Fan, Jiaxuan, Huang, Hu, Han, Te

“… Thus this paper proposes a stacked deep learning based model to represent robust feature information in terms of the advantage of cognitive computing and pattern classification theory, which is shown…”
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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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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 denoising method for ECG signals based on CEEMDAN-TSO and stacked sparse autoencoders by Li, Shun, Li, Juan, Mao, Jiandong, Hong, Wei, Sun, Ao

“…), Tuna Swarm Optimization (TSO), and Stacked Sparse Autoencoder (SSAE), named CEEMDAN-TSO-SSAE…”
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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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A Deep Learning Framework for Univariate Time Series Prediction Using Convolutional LSTM Stacked Autoencoders by Essien, Aniekan, Giannetti, Cinzia

“…) and Long Short-Term Memory (LSTM) stacked autoencoders (SAE) are combined towards single-step time series prediction…”
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A hybrid framework based on extreme learning machine, discrete wavelet transform, and autoencoder with feature penalty for stock prediction by Wu, Dingming, Wang, Xiaolong, Wu, Shaocong

“…) denoising and extreme learning machine (ELM), and promising outcomes are achieved. The current research presents a hybrid framework using DWT, ELM, and autoencoder (AE…”
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Soft Sensing for Time Series With Irregular Sampling Internals Based on a Denoising Interval Attention LSTM Network by He, Yuchen, Yang, Xueqin, Qian, Lijuan, Yao, Le, Ye, Lingjian, Wu, Ping, Ye, Gangyue, Ye, Weirong, Shen, Yafang

“… To solve the above problems, this article proposes a stacked supervised and reconstructed input denoising autoencoder integrated with internal attention long short-term memory (SSRDAE-IALSTM…”
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Multimodal fusion fault diagnosis method under noise interference by Qiu, Zhi, Fan, Shanfei, Liang, Haibo, Liu, Jincai

“… To address this issue, this paper proposes a multimodal fusion fault diagnosis method based on a multiscale stacked denoising autoencoder and dual-branch feature fusion network (MSSDAE-DBFFN…”
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Sparsity‐based autoencoders for denoising cluttered radar signatures by Ram, Shobha Sundar, Vishwakarma, Shelly, Sneh, Akanksha, Yasmeen, Kainat

“…‐dependent and target‐independent static and dynamic clutter arising from walls. A stacked and sparse denoising autoencoder (StackedSDAE…”
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Deep neural ensemble for retinal vessel segmentation in fundus images towards achieving label-free angiography by Lahiri, A., Roy, Abhijit Guha, Sheet, Debdoot, Biswas, Prabir Kumar

“… In this paper we formulate the segmentation challenge as a classification task. Specifically, we employ unsupervised hierarchical feature learning using ensemble of two level of sparsely trained denoised stacked autoencoder…”
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Adaptive data recovery model for PMU data based on SDAE in transient stability assessment by Wang, Huaiyuan, Ouyang, Yucheng

“… With current methods, noisy data are usually denoised according to expected noise. However, the real noise distribution is complex…”
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CDAE: A Cascade of Denoising Autoencoders for Noise Reduction in the Clustering of Single-Particle Cryo-EM Images by Lei, Houchao, Yang, Yang

“… In this study, we design an effective cryo-EM image denoising model, CDAE, i.e., a cascade of denoising autoencoders…”
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