Suchergebnisse - stacked denoising autoencoder neural network

Video surveillance image enhancement via a convolutional neural network and stacked denoising autoencoder von Che Aminudin, Muhamad Faris, Suandi, Shahrel Azmin

“… To address these issues, a deep learning image enhancement (DLIE) model is proposed. By utilizing a deep learning architecture such as a convolutional neural network (CNN …”
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Intelligent fault diagnosis method of rolling bearing based on stacked denoising autoencoder and convolutional neural network von Che, Changchang, Wang, Huawei, Ni, Xiaomei, Fu, Qiang

“… To solve those problems, an intelligent fault diagnosis model based on stacked denoising autoencoder (SDAE …”
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Stepwise Inertial Intelligent Control of Wind Power for Frequency Regulation Based on Stacked Denoising Autoencoder and Deep Neural Network von WANG Yalun, ZHOU Tao, CHEN Zhong, WANG Yi, QUAN Hao

“… Stepwise inertial control (SIC) provides a step-increase of power after load fluctuation, which can effectively prevent system frequency decline and ensure the …”
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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 von 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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Traffic Image Analysis Based on Stacked Denoising Autoencoder Neural Network von Kim, Daehyon

“… This study aims to explore major neural network models - Stacked Denoising Autoencoder (SDAE …”
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Application of deep neural network with stacked denoising autoencoder for ECG signal classification von Gunawan, Gunawan, Aimar Akbar, Aminnur, Andriani, Wresti

“… Applying deep neural networks with stacked denoising autoencoders (SDAEs) for ECG signal classification presents a promising approach for improving the accuracy of arrhythmia diagnosis …”
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Cost-sensitive ensemble of stacked denoising autoencoders for class imbalance problems in business domain von Wong, Man Leung, Seng, Kruy, Wong, Pak Kan

“… •Novel methods that uses deep neural networks, cost-sensitive and ensemble learning …”
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PRPI-SC: an ensemble deep learning model for predicting plant lncRNA-protein interactions von Zhou, Haoran, Wekesa, Jael Sanyanda, Luan, Yushi, Meng, Jun

“… Results In this study, we propose an ensemble deep learning model to predict plant lncRNA-protein interactions using stacked denoising autoencoder and convolutional neural network based on sequence …”
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SDARE: A stacked denoising autoencoder method for game dynamics network structure reconstruction von Huang, Keke, Li, Shuo, Dai, Penglin, Wang, Zhen, Yu, Zhaofei

“… Complex network is a general model to represent the interactions within technological, social, information, and biological interaction …”
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LIDER: cell embedding based deep neural network classifier for supervised cell type identification von Tang, Yachen, Li, Xuefeng, Shi, Mingguang

“… Based on a stacked denoising autoencoder with a tailored and reconstructed loss function, LIDER identifies cell embedding and predicts cell types with a deep neural network classifier …”
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Denoising stacked autoencoders for transient electromagnetic signal denoising von Lin, Fanqiang, Chen, Kecheng, Wang, Xuben, Cao, Hui, Chen, Danlei, Chen, Fanzeng

“… of the characteristics of the SFS to denoise the SFS. We introduce the SFSDSA (secondary field signal denoising stacked autoencoders …”
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Gated relational stacked denoising autoencoder with localized author embedding for global citation recommendation von Dai, Tao, Yan, Wenjun, Zhang, Kaiqi, Qiu, Chen, Zhao, Xiangmo, Pan, Shirui

“… This paper presents a novel neural network based model, called gated relational probabilistic stacked denoising autoencoder with localized author (GRSLA …”
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Rolling Bearing Fault Diagnosis Method Based on Stacked Denoising Autoencoder and Convolutional Neural Network von Wang, Yumin, Han, Minghong, Liu, Wei

“… A fault diagnosis method towards non-stationary signal is proposed in this paper. A fault diagnosis model of combining stacked denoising autoencoder (SDAE …”
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Low-level structure feature extraction for image processing via stacked sparse denoising autoencoder von Fan, Zunlin, Bi, Duyan, He, Linyuan, Shiping, Ma, Gao, Shan, Li, Cheng

“… In this paper, we propose a novel low-level structure feature extraction for image processing based on deep neural network, stacked sparse denoising autoencoder (SSDA …”
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Intelligent fault diagnosis approach with unsupervised feature learning by stacked denoising autoencoder von Xia, Min, Li, Teng, Liu, Lizhi, Xu, Lin, de Silva, Clarence W

“… ) based on stacked denoising autoencoder. Representative features are learned by applying the denoising autoencoder to the unlabelled data in an unsupervised manner …”
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Segmentation‐enhanced gamma spectrum denoising based on deep learning von Lu, Xiangqun, Zheng, Hongzhi, Liu, Yaqiong, Li, Hongxing, Zhou, Qingyun, Li, Tao, Yang, Hongguang

“… This paper proposes a segmentation‐enhanced Convolutional Neural Network‐Stacked Denoising Autoencoder (CNN‐SDAE …”
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Convergence Technology Opportunity Discovery for Firms Based on Technology Portfolio Using the Stacked Denoising AutoEncoder (SDAE) von Kwon, Deuksin, Sohn, So Young

“… The present research, by employing a stacked denoising autoencoder, a deep neural network-based collaborative filtering method, provides reliable latent preference toward convergence technology for individual firms …”
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A deep learning ensemble approach for crude oil price forecasting von Zhao, Yang, Li, Jianping, Yu, Lean

“… One is an advanced deep neural network model named stacked denoising autoencoders (SDAE) which is used to model the nonlinear and complex relationships of oil price with its factors …”
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A novel denoising autoencoder hybrid network for remaining useful life estimation of lithium‐ion batteries von Xia, Wei, Xu, Jinli, Liu, Baolei, Duan, Huiyun

“… ). This architecture integrates a stacked convolutional neural network with subsequent layers of bidirectional gated recurrent units within an encoder–decoder framework …”
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Recognition of cognitive load with a stacking network ensemble of denoising autoencoders and abstracted neurophysiological features von Cao, Zixuan, Yin, Zhong, Zhang, Jianhua

“… In this study, we developed a novel neural network ensemble, SE-SDAE, based on stacked denoising autoencoders (SDAEs …”
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