Suchergebnisse - Improved stack autoencoder

Fault Diagnosis of Rolling Bearings Based on an Improved Stack Autoencoder and Support Vector Machine von 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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Epileptic Seizure Recognition Using Reduced Deep Convolutional Stack Autoencoder and Improved Kernel RVFLN From EEG Signals von 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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Triple Stack Deep Variational Autoencoder For Improved Hand Gesture Recognition von Bahuguna, Arti, Bhaumik, Gopa, Govil, Mahesh Chandra

“… This paper proposes a novel approach for hand gesture recognition using a triple-stack deep variational autoencoder …”
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Intrusion Detection Model Based on SAE and BALSTM von Jiajia, Fan, Jiangfeng, Xu, Junfeng, Zhang

“… , an intrusion detection model based on improved Stack autoencoder (SAE) and bidirectional feature attention short-time memory network (BALSTM) is proposed …”
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Anomaly detection of industrial multi-sensor signals based on enhanced spatiotemporal features von Jiang, Lin, Xu, Hang, Liu, Jinhai, Shen, Xiangkai, Lu, Senxiang, Shi, Zhan

“… Then, a stack spatial–temporal autoencoder, which relies on the improved …”
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Data Quality Improvement Method for Power Equipment Condition Based on Stacked Denoising Autoencoders Improved by Particle Swarm Optimization von JI Rong, HOU Huijuan, SHENG Gehao, ZHANG Lijing, SHU Bo, JIANG Xiuchen

“… In order to solve this problem, a data cleaning method based on improved stack noise reduction autoencoder is proposed in this paper …”
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Flatness pattern recognition based on stacked sparse denoising autoencoder and improved Osprey optimisation algorithm kernel-extreme learning machine von Zhou, Yaluo, Zhang, Shaochuan, Liu, Wenguang, Zhang, Ruicheng

“… proposes a flatness recognition method based on stack sparse denoising autoencoder (SSDAE) with improved Osprey optimisation algorithm kernel-extreme learning machine …”
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Remaining useful life prognosis of turbofan engines based on deep feature extraction and fusion von Peng, Cheng, Chen, Yufeng, Gui, Weihua, Tang, Zhaohui, Li, Changyun

“… , and poor remaining useful life (RUL) prognosis effects, a remaining useful life prognosis model combining an improved stack sparse autoencoder (imSSAE …”
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Research on a fault-diagnosis strategy of lithium iron phosphate battery in an energy-storage system based on multi-feature fusion von Wang, Hongzhe, Wei, Chengjun, Zhu, Tao, Zhang, Bingyao, Cheng, Fangjie, Guo, Zhipeng, Liao, Qiangqiang

“… The features such as improved stack autoencoder, improved standard deviation and improved Shannon entropy of the voltage sequences in a sliding-time window are mutually validated to identify open …”
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Fault Diagnosis of Pitch System of Wind Turbine based on Improved Stacked Auto-Encoder Network von Wang, Sihua, Wang, Tian, Zhou, Lijun, Zhou, Lijun, Chen, Tianyu, Wang, Yu

“… In order to improve the accuracy of fault diagnosis of the wind turbine's pitch system, an improved stack autoencoder network is proposed …”
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Vehicle type classification using graph ant colony optimizer based stack autoencoder model von Rani, B. Kavitha, Rao, M. Varaprasad, Patra, Raj Kumar, Srinivas, K., Madhukar, G.

“… ), and CDnet2014 dataset. Additionally, the contrast and visible level of the video frames are improved by implementing histogram equalization method …”
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Deep Convolutional Stack Autoencoder of Process Adaptive VMD Data With Robust Multikernel RVFLN for Power Quality Events Recognition von 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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BrainNet: a fusion assisted novel optimal framework of residual blocks and stacked autoencoders for multimodal brain tumor classification von Ullah, Muhammad Sami, Khan, Muhammad Attique, Almujally, Nouf Abdullah, Alhaisoni, Majed, Akram, Tallha, Shabaz, Mohammad

“… A significant issue in computer-aided diagnosis (CAD) for medical applications is brain tumor classification. Radiologists could reliably detect tumors using …”
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A hybrid framework for predicting the remaining useful life of battery using Gaussian process regression von Li, Jiabo, Ye, Min, Wang, Yan, Wang, Qiao, Wei, Meng

“… ) based on automatic stack autoencoder (SAE) and improved whale optimization algorithm (WOA) is proposed in this research paper …”
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Classification method for imbalanced LiDAR point cloud based on stack autoencoder von 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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A deep model for short-term load forecasting applying a stacked autoencoder based on LSTM supported by a multi-stage attention mechanism von Fazlipour, Zahra, Mashhour, Elaheh, Joorabian, Mahmood

“… •A stack of autoencoders is developed to extract more latent relevant features.•The challenge of setting the random initial weights of the deep models is solved …”
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An Improved Deep Learning Model for DDoS Detection Based on Hybrid Stacked Autoencoder and Checkpoint Network von Mousa, Amthal K., Abdullah, Mohammed Najm

“… The software defined network (SDN) collects network traffic data and proactively manages networks. SDN’s programmability makes it excellent for developing …”
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A spatial-compositional feature fusion convolutional autoencoder for multivariate geochemical anomaly recognition von Guan, Qingfeng, Ren, Shuliang, Chen, Lirong, Feng, Bin, Yao, Yao

“… Based on the multitask stack autoencoder structure, this study proposes a feature fusion convolutional autoencoder (FCAE …”
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Intrusion Detection Model for Industrial Internet of Things Based on Improved Autoencoder von Zhang, Wumei, Zhang, Yongzhen

“… solve the problem of unbalanced network traffic data. The cascaded network structure is adopted to stack its sparse autoencoder network model, so as to improve the data ability of the detection …”
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Multi‐objective auto‐encoder deep learning‐based stack switching scheme for improved battery life using error prediction of wind‐battery storage microgrid von Mishra, Sthita Prajna, Krishna Rayi, Vijaya, Dash, Pradipta Kishore, Bisoi, Ranjeeta

“… Summary For any wind power generation system, battery energy storage is a suitable backup power unit for ensuring greater functionality by compensating the …”
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