Search Results - stack denoising autoencoder*

BePCon: A Photoplethysmography-based Quality-aware Continuous Beat-to-Beat Blood Pressure Measurement Technique Using Deep Learning by Roy, Monalisa Singha, Gupta, Rajarshi, Sharma, Kaushik Das

“…). Initially, the signal quality assessment (SQA) of PPG is done by a self-organizing map (SOM). Next, the time-domain, statistical, wavelet and stacked autoencoder features from current and previous good quality PPG cycles are extracted…”
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SDAE-LFM: A Latent Factor Model for Recommendation Based on Stack Denoising AutoEncoder by Luo, Jianyan, Xing, Xing, Zheng, Hang, Xin, Mindong, Jia, Zhichun

“… In order to deal with these problems, we proposed a latent factor model recommendation algorithm based on stack denoising autoencoder (SDAE-LFM…”
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Long baseline underwater source localization based on deep K-Means++ clustering in complex underwater environments by Dai, Yawen, Yang, Lei, Cao, Yifei

“… A three-layer stack denoising autoencoder (SDA) was utilized to extract the features of the trilateration results…”
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An Intelligent Fault Diagnosis Method for Reciprocating Compressors Based on LMD and SDAE by Liu, Yang, Duan, Lixiang, Yuan, Zhuang, Wang, Ning, Zhao, Jianping

“…) and the Stack Denoising Autoencoder (SDAE). The vibration signal is firstly decomposed by LMD and reconstructed…”
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SDCA: a novel stack deep convolutional autoencoder – an application on retinal image denoising by Ghosh, Swarup Kr, Biswas, Biswajit, Ghosh, Anupam

“… This study represents a deep learning based approach to denoising images and restoring features using stack denoising convolutional autoencoder…”
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On-Device Reliability Assessment and Prediction of Missing Photoplethysmographic Data Using Deep Neural Networks by Singha Roy, Monalisa, Roy, Biplab, Gupta, Rajarshi, Das Sharma, Kaushik

“… This paper describes an on-device reliability assessment from PPG measurements using a stack denoising autoencoder (SDAE…”
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Stack Denoising Autoencoder and State-Space Model Based Bearing RUL Prediction Method by Yang, Lei, Xu, Cuiwentong, Duan, Rongkai, Liao, Yuhe

“… To solve the problems, a RUL prediction method based on stack denoising autoencoder (SDA) and non-overlapping sliding window…”
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A Hybrid Three-Staged, Short-Term Wind-Power Prediction Method Based on SDAE-SVR Deep Learning and BA Optimization by Duan, Ruiqin, Peng, Xiaosheng, Li, Cong, Yang, Zimin, Jiang, Yan, Li, Xiufeng, Liu, Shuangquan

“… In order to improve the prediction accuracy of WPP, in this paper we propose a three-step model named SDAE-SVR-BA to be applied in short-term WPP based on stacked-denoising-autoencoder (SDAE…”
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SDCAE: Stack Denoising Convolutional Autoencoder Model for Accident Risk Prediction Via Traffic Big Data by Chen, Chao, Fan, Xiaoliang, Zheng, Chuanpan, Xiao, Lujing, Cheng, Ming, Wang, Cheng

“… However, predicting the risk of citywide accidents remains an open issue. To address this problem, we propose SDCAE, a novel Stack Denoise Convolutional Auto-Encoder algorithm to predict the risk of traffic accident in the city-level…”
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Marginalised stack denoising autoencoders for metagenomic data binning by Kouchaki, Samaneh, Tirunagari, Santosh, Tapinos, Avraam, Robertson, David L.

“… LBP codes represent the data in a sparse high dimensional space. To improve the performance of our pipeline, marginalised stacked autoencoders are used here…”
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Intelligent Fault Diagnosis Method for Blade Damage of Quad-Rotor UAV Based on Stacked Pruning Sparse Denoising Autoencoder and Convolutional Neural Network by Yang, Pu, Wen, Chenwan, Geng, Huilin, Liu, Peng

“…This paper introduces a new intelligent fault diagnosis method based on stack pruning sparse denoising autoencoder and convolutional neural network (sPSDAE-CNN…”
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Nondestructive detection of cadmium content in oilseed rape leaves under different silicon environments using deep transfer learning and Vis-NIR hyperspectral imaging by Zhou, Xin, Liu, Yang, Zhao, Chunjiang, Sun, Jun, Shi, Lei, Cong, Sunli

“…In this paper, a transfer stack denoising autoencoder (T-SDAE) algorithm is proposed to implement the migration of cadmium (Cd…”
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Application of stack marginalised sparse denoising auto-encoder in fault diagnosis of rolling bearing by Zhang, Junling, Chen, Zhigang, Du, Xiaolei, Xu, Xu, Yu, Miao

“… For this special working environment, this study proposes a rolling bearing fault diagnosis method based on stack marginalised sparse denoising auto-encoder (SDAE…”
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Flight delay prediction based on deep learning and Levenberg-Marquart algorithm by Yazdi, Maryam Farshchian, Kamel, Seyed Reza, Chabok, Seyyed Javad Mahdavi, Kheirabadi, Maryam

“…Flight delay is inevitable and it plays an important role in both profits and loss of the airlines. An accurate estimation of flight delay is critical for…”
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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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Denoising stacked autoencoders‐based near‐infrared quality monitoring method via robust samples evaluation by Lv, Jiapeng, Chen, Zihao, Luan, Xiaoli, Liu, Fei

“…). First, this paper aims to achieve feature extraction in nonlinearity with a denoising stack autoencoder, overcoming the impact…”
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Deep learning-based protoacoustic signal denoising for proton range verification by Wang, Jing, Sohn, James J, Lei, Yang, Nie, Wei, Zhou, Jun, Avery, Stephen, Liu, Tian, Yang, Xiaofeng

“…Proton therapy is a type of radiation therapy that can provide better dose distribution compared to photon therapy by delivering most of the energy at the end…”
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Network intrusion detection based on Contractive Sparse Stacked Denoising Autoencoder by Lu, Jizhao, Meng, Huiping, Li, Wencui, Liu, Yue, Guo, Yihao, Yang, Yang

“… identify and classify network intrusion data, this paper proposes a Contractive Sparse Stack Denoising Autoencoder(CSSDAE…”
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Yield and Properties Prediction Based on the Multicondition LSTM Model for the Solvent Deasphalting Process by Long, Jian, Chen, Yifan, Cao, Dengke, Chen, Pengyu, Yang, Minglei

“… Then, the stack denoising autoencoder (SDAE) is used to reconstruct the data and obtain the nonlinear mapping information of hidden layers of SDAE and achieve feature dimension reduction…”
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Research on tool condition monitoring (TCM) using a novel unsupervised deep neural network (DNN) by Gao, Jingjing, Liu, Jing, Yu, Xinli

“…) based on stack denoising autoencoder (SDA) is proposed. After feature extraction and selection, the stack denoising automatic coding network reduces the dimensionality of the feature vector…”
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