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检索条件"主题词=autoencoder"
4298 条 记 录,以下是1041-1050 订阅
Depression status identification using autoencoder neural network
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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2022年 第0期75卷 103568-103568页
作者: Sharma, Vivek Prakash, Neelam Rup Kalra, Parveen Lovely Profess Univ Dept Prod & Ind Design Phagwara India Punjab Engn Coll Dept Elect & Commun Chandigarh India Punjab Engn Coll Dept Prod & Ind Engn Chandigarh India
Depression is the leading mental illness/disorder around the world with global number reaching up to 300 million worldwide. This mental disorder is more prevalent in youngster of age group of 18-25 years especially in... 详细信息
来源: 评论
Binarization Strategy Using Multiple Convolutional autoencoder Network for Old Sundanese Manuscript Images  16th
Binarization Strategy Using Multiple Convolutional Autoencod...
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16th IAPR International Conference on Document Analysis and Recognition (ICDAR)
作者: Paulus, Erick Burie, Jean-Christophe Verbeek, Fons J. Leiden Univ Leiden Inst Adv Comp Sci Leiden Netherlands Univ La Rochelle Lab Informat Image Interact L3i Ave Michel Crepeau F-17042 La Rochelle 1 France Univ Padjadajaran Comp Sci Dept Bandung Indonesia
The binarization step for old documents is still a challenging task even though many hand-engineered and deep learning algorithms have been offered. In this research work, we address foreground and background segmenta... 详细信息
来源: 评论
Power Quality Disturbances Classification Using Sparse autoencoder (SAE) Based on Deep Neural Network  11
Power Quality Disturbances Classification Using Sparse Autoe...
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11th IEEE Symposium on Computer Applications and Industrial Electronics (ISCAIE)
作者: Manan, Nurul Asiah Shahbudin, Shahrani Kassim, Murizah Mohamad, Roslina Rahman, Farah Yasmin Abdul Univ Teknol MARA Fac Elect Engn Shah Alam 40450 Selangor Malaysia
Power quality is main concern for the electrical energy consumptions and electrical equipment. Hence, the power quality disturbances needed to monitor, improve and control. However, most of the research are focusing t... 详细信息
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Graph dynamic autoencoder for fault detection
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CHEMICAL ENGINEERING SCIENCE 2022年 254卷
作者: Liu, Lu Zhao, Haitao Hu, Zhengwei East China Univ Sci & Technol Sch Informat Sci & Engn Automat Dept Shanghai Peoples R China
Dynamic information is a non-negligible part of time-correlated process data, and its full utilization can improve the performance of fault detection. Traditional dynamic methods concatenate the current process data w... 详细信息
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Comparison of autoencoder architectures for fault detection in industrial processes
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DIGITAL CHEMICAL ENGINEERING 2024年 12卷
作者: Spina, Deris Eduardo Campos, Luiz Felipe de O. de Arruda, Wallthynay F. Melo, Afranio Alves, Marcelo F. de S. Rabello, Gildeir Lima Anzai, Thiago K. Pinto, Jose Carlos Univ Fed Rio De Janeiro Programa Engn Quim COPPE BR-21941972 Rio De Janeiro RJ Brazil Univ Fed Rio De Janeiro Programa Pos Grad Engn Proc Quim & Bioquim EPQB BR-21941909 Rio De Janeiro RJ Brazil Petrobras Petr Brasileiro SA Ctr Pesquisas Leopoldo Americo Miguez Mello CENPES BR-21941915 Rio De Janeiro RJ Brazil
Fault detection constitutes a fundamental task for predictive maintenance, requiring mathematical models that can be conveniently provided by data -driven techniques. autoencoders are a particular type of unsupervised... 详细信息
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A convolutional autoencoder framework for ECG signal analysis
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Heliyon 2025年 第2期11卷 e41517页
作者: Lomoio, Ugo Vizza, Patrizia Giancotti, Raffaele Petrolo, Salvatore Flesca, Sergio Boccuto, Fabiola Guzzi, Pietro Hiram Veltri, Pierangelo Tradigo, Giuseppe Department of Surgical and Medical Sciences University of Catanzaro Catanzaro Italy DIMES University of Calabria Rende Italy SMARTEST Laboratory E-Campus University Novedrate Italy Division of Cardiology Department of Surgical and Medical Sciences University of Catanzaro Catanzaro Italy
Electrocardiographic (ECG) signals are used to evaluate heart activity and to identify disease-related anomalies. Reliable support systems are useful for analyzing ECG signals, for instance, in long-term data acquisit... 详细信息
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ROBUST GRAPH autoencoder FOR HYPERSPECTRAL ANOMALY DETECTION
ROBUST GRAPH AUTOENCODER FOR HYPERSPECTRAL ANOMALY DETECTION
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IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
作者: Fan, Ganghui Ma, Yong Huang, Jun Mei, Xiaoguang Ma, Jiayi Wuhan Univ Elect Informat Sch Wuhan 430072 Peoples R China Wuhan Univ Inst Aerosp Sci & Technol Wuhan 430072 Peoples R China
autoencoder can not only extract features in an unsupervised manner, but also selects samples out that differs significantly from others. However, autoencoder is sensitive to noise and anomalies during training, and t... 详细信息
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Brain Tissue Microstructure Characterization Using dMRI Based autoencoder Neural-Networks  12th
Brain Tissue Microstructure Characterization Using dMRI Base...
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12th International Workshop on Computational Diffusion MRI (CDMRI)
作者: Zucchelli, Mauro Deslauriers-Gauthier, Samuel Deriche, Rachid Univ Cote dAzur INRIA Sophia Antipolis France
In recent years, multi-compartmental models have been widely used to try to characterize brain tissue microstructure from Diffusion Magnetic Resonance Imaging (dMRI) data. One of the main drawbacks of this approach is... 详细信息
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Deep autoencoder-based Massive MIMO CSI Feedback with Quantization and Entropy Coding
Deep Autoencoder-based Massive MIMO CSI Feedback with Quanti...
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IEEE Global Communications Conference (GLOBECOM)
作者: Ravula, Sriram Jain, Swayambhoo Univ Texas Austin Dept Elect & Comp Engn Austin TX 78712 USA Microsoft Sunnyvale CA USA
Techniques which leverage channel state information (CSI) at a transmitter to adapt wireless signals to changing propagation conditions have been shown to improve the reliability of modern multiple input multiple outp... 详细信息
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A Weight-Sharing autoencoder with Dynamic Quantization for Efficient Feature Compression  12
A Weight-Sharing Autoencoder with Dynamic Quantization for E...
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12th International Conference on ICT Convergence (ICTC) - Beyond the Pandemic Era with ICT Convergence Innovation
作者: Choi, Ji Sub Kim, Jungrae Ko, Jong Hwan Sungkyunkwan Univ Dept Elect & Comp Engn Suwon South Korea Sungkyunkwan Univ Coll Informat & Commun Engn Suwon South Korea
Collaborative inference (CI) enhances the inference efficiency of deep neural networks (DNNs) by partitioning a computational workload between an edge device and a cloud platform. Efficient inference using CI requires... 详细信息
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