Výsledky vyhľadávania - residual conventional autoencoder

ConvAE: A New Channel Autoencoder Based on Convolutional Layers and Residual Connections Autor Ji, Dong Jin, Park, Jinsol, Cho, Dong-Ho

“…In this letter, we propose ConvAE, a new channel autoencoder structure. ConvAE uses residual blocks with convolutional layers…”
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Health indicator for machine condition monitoring built in the latent space of a deep autoencoder Autor González-Muñiz, Ana, Díaz, Ignacio, Cuadrado, Abel A., García-Pérez, Diego

“… such as reduced maintenance costs, improved productivity or increased machine availability. The reconstruction error of deep autoencoders has been widely used…”
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Memory Residual Regression Autoencoder for Bearing Fault Detection Autor Huang, Xin, Wen, Guangrui, Dong, Shuzhi, Zhou, Haoxuan, Lei, Zihao, Zhang, Zhifen, Chen, Xuefeng

“… In this article, an innovative deep-learning-based model, namely, memory residual regression autoencoder (MRRAE…”
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Attention-Empowered Residual Autoencoder for End-to-End Communication Systems Autor Lu, Min, Zhou, Bin, Bu, Zhiyong

“… Therefore, we first adopt a residual attention module to enhance the representation ability of the autoencoder, where channel and spatial attention focus on finding fine-grained clues between signals and noise…”
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Autoencoder-driven fault detection and diagnosis in building automation systems: Residual-based and latent space-based approaches Autor Choi, Youngwoong, Yoon, Sungmin

“… Conventional AE-based FDD methods can use two types of information generated from the novel structure of the AE: (1) residual matrix (REM) and (2…”
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Deep Residual Autoencoders for Expectation Maximization-Inspired Dictionary Learning Autor Tolooshams, Bahareh, Dey, Sourav, Ba, Demba

“…We introduce a neural-network architecture, termed the constrained recurrent sparse autoencoder (CRsAE…”
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A combination method of stacked autoencoder and 3D deep residual network for hyperspectral image classification Autor Zhao, Jinling, Hu, Lei, Dong, Yingying, Huang, Linsheng, Weng, Shizhuang, Zhang, Dongyan

“…In comparison with conventional machine learning algorithms, deep learning can effectively express the deep features of remote sensing images…”
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A residual autoencoder-based transformer for fault detection of multivariate processes Autor Shang, Jilin, Yu, Jianbo

“… In this paper, a new transformer model, residual autoencoder-based transformer, is proposed for process fault detection…”
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Dual Weight Vector-driven Iterative Reinforced Fuzzy Clustering-based Network Architecture by Autoencoder-based Interval Weighting Strategy and Residual-based Tournament Selection Mechanism Autor Zhang, Congcong, Fu, Zunwei, Oh, Sung-Kwun, Pedrycz, Witold

“…) designed to enhance the generalization performance of conventional Fuzzy Clustering-based Neural Networks (FCNNs…”
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Missing data imputation and sensor self-validation towards a sustainable operation of wastewater treatment plants via deep variational residual autoencoders Autor Ba-Alawi, Abdulrahman H., Loy-Benitez, Jorge, Kim, SangYun, Yoo, ChangKyoo

“…) sensors are crucial for energy conservation and environmental protection. Given the dynamic and non-linear characteristics of WWTP measurements, the conventional…”
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Unsupervised feature learning based on autoencoder for epileptic seizures prediction Autor He, Peng, Wang, Linhai, Cui, Yaping, Wang, Ruyan, Wu, Dapeng

“… Conventional technologies of EEG analysis mostly utilize supervised learning methods with a mass of labeled data, which bring leakage risks to healthcare data. In addition…”
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Threat Detection Framework for IoT Devices Using Adaptive Dilated Conv‐Gated Recurrent Units With Spatial and Temporal Attention‐Based Residual Autoencoder Autor Shirode, Ujwal Ramesh, Vhatkar, Kapil Netaji, Sontakke, Prakash V., Pawar, Aarti S., Salunke, Dipmala

“…ABSTRACT Purpose Internet of Things (IoT) network is equipped with sensors, actuators, and act as communication capabilities. It enables device to collect,…”
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Optimal Spectrum Sensing Framework for Cognitive Radio Networks Using Attention‐Based Autoencoder With Multi‐Scale Capsule Network Autor Rao, R. Koteswara, Sahaai, Madona B.

“… In addition, the conventional models cause high computational complexity in the SS. In this work, an adaptive SS system for CR networks is developed to identify unused bands of frequencies in order to get outside of these constraints…”
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ASE-RAE: an attention-based squeeze-excitation residual autoencoder for medical image denoising Autor Babu, T., Murthy, H S Niranjana, G, Surya, M, Muthalakshmi

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Reducing variations in multi-center Alzheimer’s disease classification with convolutional adversarial autoencoder Autor Cobbinah, Bernard M., Sorg, Christian, Yang, Qinli, Ternblom, Arvid, Zheng, Changgang, Han, Wei, Che, Liwei, Shao, Junming

“…Based on brain magnetic resonance imaging (MRI), multiple variations ranging from MRI scanners to center-specific parameter settings, imaging protocols, and…”
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Multiscale Feature-Clustering-Based Fully Convolutional Autoencoder for Fast Accurate Visual Inspection of Texture Surface Defects Autor Yang, Hua, Chen, Yifan, Song, Kaiyou, Yin, Zhouping

“… or their time-consuming sliding-window strategy. In this paper, we present a novel unsupervised multiscale feature-clustering-based fully convolutional autoencoder (MS-FCAE…”
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A novel unsupervised deep learning method for the generalization of urban form Autor Cai, Jihong, Chen, Yimin

“… The novelty of the proposed method is the Multiscale Residual Convolutional Autoencoder (MRCAE), which can learn the latent representation of different…”
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LSO-RVAE: latent space optimization with sparrow search and residual variational autoencoder for Credit card fraud detection Autor Shabiya, M I, Chandrika, R Roopa

“… To address these challenges, we propose a hybrid model, Latent Space Optimisation with Residual Variational Autoencoder (LSO-RVAE…”
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CResDAE: A Deep Autoencoder with Attention Mechanism for Hyperspectral Unmixing Autor Zhao, Chong, Wang, Jinlin, Qiao, Qingqing, Zhou, Kefa, Bi, Jiantao, Zhang, Qing, Wang, Wei, Li, Dong, Liao, Tao, Li, Chao, Qiu, Heshun, Qu, Guangjun

“… To address these limitations, this paper proposes a novel model, CResDAE, based on a deep autoencoder architecture…”
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Autoencoder-Based DIFAR Sonobuoy Signal Transmission and Reception Method Incorporating Residual Vector Quantization and Compensation Module: Validation Through Air Channel Modeling Autor Park, Yeonjin, Hong, Jungpyo

“…% compared to the conventional autoencoder-based method. Moreover, the spectral mean square errors are reduced by 60.58% for continuous wave signals and 55.25% for linear frequency modulation signals under realistic air channel simulations…”
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