Suchergebnisse - stacked denoising autoencoder network

Deep learning for electrolysis process anode effect prediction based on long short-term memory network and stacked denoising autoencoder von Yin, Gang, Li, Yi-Hui, Yan, Fei-Ya, Quan, Peng-Cheng, Wang, Min, Cao, Wen-Qi, Xu, Heng-Quan, Lu, Jian, He, Wen

“… A stacked denoising autoencoder is utilized to denoise and extract features from a large number of long-period parameter data …”
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Wind turbine generator early fault diagnosis using LSTM-based stacked denoising autoencoder network and stacking algorithm von Yan, Junshuai, Liu, Yongqian, Meng, Hang, Li, Li, Ren, Xiaoying

“… Specifically, a multivariate spatiotemporal condition monitoring model (LSDAE) was established by combining the LSTM and SDAE networks, which can detect generator early anomalies through real-time monitoring the reconstruction residual …”
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Identification Method for Missing Cotter Pins in Catenary Systems Based on Stacked Denoising Autoencoder Network von SHAN Chonghao

“… (stacked denoising autoencoder), thereby achieving efficient detection of missing cotter pins …”
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Video surveillance image enhancement via a convolutional neural network and stacked denoising autoencoder von Che Aminudin, Muhamad Faris, Suandi, Shahrel Azmin

“… ) and a denoising autoencoder, the image quality can be enhanced. The DLIE …”
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Automated atrial fibrillation classification based on denoising stacked autoencoder and optimized deep network von Singh, Prateek, Sharma, Ambalika, Maiya, Shreesha

“… Denoising autoencoders (DAEs) utilising convolutional neural networks (CNNs) as backbones outperformed the other two DAE models …”
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Visual tracking based on stacked Denoising Autoencoder network with genetic algorithm optimization von Hua, Weixin, Mu, Dejun, Guo, Dawei, Liu, Hang

“… There are two training stages for the SDAE network: Layer-wise pre-training and fine-tuning. In the pre-training stage, a two-layer sparse-coded method is used to represent the input image, then a multi-level image feature descriptor is obtained …”
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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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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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Automatic Diagnosis of Microgrid Networks’ Power Device Faults Based on Stacked Denoising Autoencoders and Adaptive Affinity Propagation Clustering von Fan, Bo, Zhang, Xiaodi, Shu, Xin, Xu, Fan

“… This paper presents a model based on stacked denoising autoencoders (SDAEs) in deep learning and adaptive affinity propagation (adAP …”
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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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Demodulating Optical Wireless Communication of FBG Sensing with Turbulence-Caused Noise by Stacked Denoising Autoencoders and the Deep Belief Network von Bogale, Shegaw Demessie, Yao, Cheng-Kai, Manie, Yibeltal Chanie, Dehnaw, Amare Mulatie, Tefera, Minyechil Alehegn, Li, Wei-Long, Zhong, Zi-Gui, Peng, Peng-Chun

“… This work aims to deep denoise sensed signals from fiber Bragg grating (FBG) sensors based on FSO link transmission using advanced denoising deep learning techniques, such as stacked denoising autoencoders (SDAE …”
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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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Network intrusion detection based on Contractive Sparse Stacked Denoising Autoencoder von Lu, Jizhao, Meng, Huiping, Li, Wencui, Liu, Yue, Guo, Yihao, Yang, Yang

“… identify and classify network intrusion data, this paper proposes a Contractive Sparse Stack Denoising Autoencoder(CSSDAE …”
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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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Data-Driven Bearing Fault Diagnosis of Microgrid Network Power Device Based on a Stacked Denoising Autoencoder in Deep Learning and Clustering by Fast Search without Data Labels von Zhang, Xiaodi, Li, Xin, Shu, Xin, Xu, Fan

“… In this paper, we use the stacked denoising autoencoder (SDAE) model in deep learning to construct HI directly from the microgrid power equipment of raw signals in bearings …”
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Fully convolutional Deep Stacked Denoising Sparse Auto encoder network for partial face reconstruction von Dinesh, P.S., Manikandan, M.

“… •By using principle component analysis and linear regression method and re-identification to the DS-DSA network …”
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Stacked Denoising Autoencoder network for short-term prediction of electrical Algerian load von Hiba, Chelabi, Tarek, Khadir Mohamed, Belkacem, Chikhaoui

“… In this paper, time series models have been developed to provide an efficient forecast for electricity consumption in Algeria using Deep Neural Networks in the form of Stacked Denoising Autoencoder (SDAE …”
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Social Network Embedding Method Combining Node Attributes and Loop-Free Path von WANG Benyu, GU Yijun, PENG Shufan

“… Network embedding’s goal is to learn the low-dimensional node feature representation in the network …”
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Partial Discharge Patterns Recognition of GIS with Denoising-stacked Autoencoder Networks von Zhao, Yiming, Yan, Jing, Wang, Yanxin, Liu, Tingliang, Jiang, Junjie

“… This paper proposes a pattern recognition classifier that directly and automatically selects and classifies fault features by denoising-stacked autoencoder …”
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