Výsledky vyhledávání - "stack autoencoder"

Fault Diagnosis of Rolling Bearings Based on an Improved Stack Autoencoder and Support Vector Machine Autor 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 Autor 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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Epileptic Seizure Recognition Using Reduced Deep Convolutional Stack Autoencoder and Improved Kernel RVFLN From EEG Signals Autor 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 Autor 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 Autor 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 Autor 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 Autor 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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Classification method for imbalanced LiDAR point cloud based on stack autoencoder Autor 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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Application of deep stack autoencoder network in ship weight estimation Autor 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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Electricity theft detection based on stacked sparse denoising autoencoder Autor 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…”
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SAE+LSTM: A New Framework for Emotion Recognition From Multi-Channel EEG Autor 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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Damage characterization using CNN and SAE of broadband Lamb waves Autor 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 Autor 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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Robust broad learning system with parametrized variational mode decomposition for schizophrenia diagnosis Autor 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…”
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Real-Time Forecasting of Subsurface Inclusion Defects for Continuous Casting Slabs: A Data-Driven Comparative Study Autor 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…”
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Short-Term Streamflow Forecasting Using the Feature-Enhanced Regression Model Autor 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…”
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Research on the practical strategy of ideological and political education in colleges and universities based on thinking operation model Autor 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…”
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Anomaly detection of industrial multi-sensor signals based on enhanced spatiotemporal features Autor 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…”
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An Efficient Mutual Authentication and Fractional Lyrebird Optimization With Deep Learning–Based SIP‐Based DRDoS Attack Detection Autor Sreenivasulu, V., Ravikumar, C. V.

“… In this article, a deep learning model fractional lyrebird optimization algorithm–deep stack autoencoder (FLOA‐DSA…”
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Incorporating Heterogeneous Features into the Random Subspace Method for Bearing Fault Diagnosis Autor 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…”
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