Search Results - stack autoencoder*

Fault Diagnosis of Rolling Bearings Based on an Improved Stack Autoencoder and Support Vector Machine by Cui, Mingliang, Wang, Youqing, Lin, Xinshuang, Zhong, Maiying

“… To solve this issue, this study proposes a feature distance stack autoencoder (FD-SAE) for rolling bearing fault diagnosis…”
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Stack Autoencoder Transfer Learning Algorithm for Bearing Fault Diagnosis Based on Class Separation and Domain Fusion by Sun, Meidi, Wang, Hui, Liu, Ping, Huang, Shoudao, Wang, Pan, Meng, Jinhao

“… In this article, we propose a stack autoencoder transfer learning algorithm based on the class separation and domain fusion (SAE-CSDF…”
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A Just-in-Time Fine-Tuning Framework for Deep Learning of SAE in Adaptive Data-Driven Modeling of Time-Varying Industrial Processes by Wu, Yijun, Liu, Diju, Yuan, Xiaofeng, Wang, Yalin

“… This may cause their performance degradation in time-varying processes. To deal with this problem, an adaptive updating framework is proposed for deep learning, which is based on just-in-time fine-tuning of stacked autoencoder (JIT-SAE…”
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Epileptic Seizure Recognition Using Reduced Deep Convolutional Stack Autoencoder and Improved Kernel RVFLN From EEG Signals by Sahani, Mrutyunjaya, Rout, Susanta Kumar, Dash, Pradipta Kishor

“…In this paper, reduced deep convolutional stack autoencoder (RDCSAE) and improved kernel random vector functional link network (IKRVFLN…”
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Vehicle type classification using graph ant colony optimizer based stack autoencoder model by Rani, B. Kavitha, Rao, M. Varaprasad, Patra, Raj Kumar, Srinivas, K., Madhukar, G.

“…In the intelligent transport system, vehicle type classification technology plays a major role. With the growth of video processing and pattern recognition…”
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Precise single step and multistep short-term photovoltaic parameters forecasting based on reduced deep convolutional stack autoencoder and minimum variance multikernel random vector functional network by Sahani, Mrutyunjaya, Choudhury, Sasmita, Siddique, Marif Daula, Parida, Tanmoy, Dash, Pradipta Kishore, Panda, Sanjib Kumar

“… To address this, we have developed a novel hybrid model: a reduced deep convolutional stack autoencoder with a minimum variance multikernel random vector functional link network (RDCSAE-MVMRVFLN…”
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Deep Convolutional Stack Autoencoder of Process Adaptive VMD Data With Robust Multikernel RVFLN for Power Quality Events Recognition by Sahani, Mrutyunjaya, Dash, Pradipta Kishore

“…). A novel reduced deep convolutional neural network (RDCNN) embedded with stack autoencoder, that is, RDCSAE structure is introduced to extract the most discriminative…”
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Damage assessments of composite under the environment with strong noise based on synchrosqueezing wavelet transform and stack autoencoder algorithm by Su, Chenhui, Jiang, Mingshun, Liang, Jianying, Tian, Aiqin, Sun, Lin, Zhang, Lei, Zhang, Faye, Sui, Qingmei

“… This study proposes a technique for damage location and quantitative identification for composites under strong noise background on the basis of synchro squeezing wavelet transform and stack autoencoder algorithm…”
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Application of deep stack autoencoder network in ship weight estimation by CHEN Jian,TANG Junyao,ZHU Shengguang,ZHOU Zhaozhao

“… the parameters.The deep stack autoencoder network is used to mine the deep data features and do analysis on the ShipWE self-built…”
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Classification method for imbalanced LiDAR point cloud based on stack autoencoder by Ren, Peng, Xia, Qunli

“… Therefore, by studying the existing deep network structure and imbalanced sampling methods, this paper proposes an oversampling method based on stack autoencoder…”
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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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A new Stack Autoencoder: Neighbouring Sample Envelope Embedded Stack Autoencoder Ensemble Model by Zhou, Chuanyan, Ma, Jie, Li, Fan, Li, Yongming, Wang, Pin, Zhang, Xiaoheng

“…Stack autoencoder (SAE), as a representative deep network, has unique and excellent performance in feature learning, and has received extensive attention from researchers…”
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Electricity theft detection based on stacked sparse denoising autoencoder by Huang, Yifan, Xu, Qifeng

“…Inspired by the powerful feature extraction and the data reconstruction ability of autoencoder, a stacked sparse denoising autoencoder is developed for electricity theft detection in this paper…”
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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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SAE+LSTM: A New Framework for Emotion Recognition From Multi-Channel EEG by Xing, Xiaofen, Li, Zhenqi, Xu, Tianyuan, Shu, Lin, Hu, Bin, Xu, Xiangmin

“… Specially, Stack AutoEncoder (SAE) is used to build and solve the linear EEG mixing model and the emotion timing model is based on the Long Short-Term Memory Recurrent Neural Network (LSTM-RNN…”
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Fault diagnosis of brushless DC motor based on Stack Sparse Autoencoder by Du, Qiang, Zhu, Danjiang, Ni, Ming

“…Because of their simple structure, long service life, high efficiency, etc., brushless DC (BLDC) motors have been widely applied in many fields. In some…”
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Stacked Autoencoders Driven by Semi-Supervised Learning for Building Extraction from near Infrared Remote Sensing Imagery by Protopapadakis, Eftychios, Doulamis, Anastasios, Doulamis, Nikolaos, Maltezos, Evangelos

“…In this paper, we propose a Stack Auto-encoder (SAE)-Driven and Semi-Supervised (SSL)-Based Deep Neural Network…”
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Damage characterization using CNN and SAE of broadband Lamb waves by Gao, Fei, Hua, Jiadong

“…) and stack autoencoder (SAE) are promising to extract features from Lamb wave signals that can be linked with damage for subsequent localization and quantification…”
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Deep Adversarial Domain Adaptation Model for Bearing Fault Diagnosis by Liu, Zhao-Hua, Lu, Bi-Liang, Wei, Hua-Liang, Chen, Lei, Li, Xiao-Hua, Ratsch, Matthias

“…: the source domain and the target domain are inconsistent in their distribution. First, a deep stack autoencoder (DSAE…”
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Research on HRRP target recognition based on one-dimensional stack convolutional autoencoder by Zhang, Guoling, Wang, Xiaodan, Li, Rui, Lai, Jie, Xiang, Qian

“… Aiming at the problem of feature extraction and recognition in HRRP target recognition, a one-dimensional stack convolutional autoencoder (1D-sCAE…”
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