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…”
Get full text

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…”
Get full text

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…”
Get full text

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…”
Get full text

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…”
Get full text

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…”
Get full text

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…”
Get full text

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…”
Get full text

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…”
Get full text

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…”
Get full text

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…”
Get full text

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…”
Get full text

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…”
Get full text

Robust broad learning system with parametrized variational mode decomposition for schizophrenia diagnosis by Parija, Sebamai, Sahani, Mrutyunjaya, Rout, Susanta Kumar

“…), which are selected using fuzzy dispersion entropy (FDE). The extracted BLIMFs are fed into deep stack autoencoder (DSAE…”
Get full text

Real-Time Forecasting of Subsurface Inclusion Defects for Continuous Casting Slabs: A Data-Driven Comparative Study by Wei, Chihang, Song, Zhihuan

“…Subsurface inclusions are one of the most common defects that affect the inner quality of continuous casting slabs. This increases the defects in the final…”
Get full text

Short-Term Streamflow Forecasting Using the Feature-Enhanced Regression Model by Bai, Yun, Bezak, Nejc, Sapač, Klaudija, Klun, Mateja, Zhang, Jin

“…), which combined stack autoencoder (SAE) with long short-term memory (LSTM). This model had two constituents…”
Get full text

Research on the practical strategy of ideological and political education in colleges and universities based on thinking operation model by Lv, Jie, Chen, Liu

“…’ learning motivation and academic achievement are selected as the main research objects. A combination of stack autoencoder and Gaussian mixture model is used to constitute a deep clustering model of learning motivation for intervention analysis…”
Get full text

Anomaly detection of industrial multi-sensor signals based on enhanced spatiotemporal features by Jiang, Lin, Xu, Hang, Liu, Jinhai, Shen, Xiangkai, Lu, Senxiang, Shi, Zhan

“…To improve the validity of industrial multi-sensor signals, anomaly detection has become a significant part of industrial signal processing. In practical…”
Get full text

An Efficient Mutual Authentication and Fractional Lyrebird Optimization With Deep Learning–Based SIP‐Based DRDoS Attack Detection by Sreenivasulu, V., Ravikumar, C. V.

“… In this article, a deep learning model fractional lyrebird optimization algorithm–deep stack autoencoder (FLOA‐DSA…”
Get full text

Incorporating Heterogeneous Features into the Random Subspace Method for Bearing Fault Diagnosis by Chu, Yan, Ali, Syed Muhammad, Lu, Mingfeng, Zhang, Yanan

“… Primarily, via signal processing methods, statistical features are extracted, and via the deep stack autoencoder (DSAE…”
Get full text