Výsledky vyhledávání - sparse convolutional autoencoder (((scae OR sae) OR cae))*

SCAE—Stacked Convolutional Autoencoder for Fault Diagnosis of a Hydraulic Piston Pump with Limited Data Samples Autor Eraliev, Oybek, Lee, Kwang-Hee, Lee, Chul-Hee

“… In this study, we propose a novel DL model based on a stacked convolutional autoencoder (SCAE) to address the challenge of limited data…”
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Sparse convolutional autoencoder‐based fault location for drive circuits in nuclear reactors Autor Yang, Cheng, Yuan, Yannan, Wang, Fu, Li, Jueying, Li, Ang, Min, Yuan, Zhang, Qiang

“…Drive circuit is a critical part of instrumentation and control systems in nuclear reactors, and its performance directly influences the operation of nuclear…”
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A Novel Convolutional Autoencoder-Based Clutter Removal Method for Buried Threat Detection in Ground-Penetrating Radar Autor Temlioglu, Eyyup, Erer, Isin

“… A new clutter removal method based on convolutional autoencoders (CAEs) is introduced. The raw GPR image is encoded via successive convolution and pooling layers and then decoded to provide the clutter-free GPR image…”
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Unsupervised Spatial-Spectral Feature Learning by 3D Convolutional Autoencoder for Hyperspectral Classification Autor Mei, Shaohui, Ji, Jingyu, Geng, Yunhao, Zhang, Zhi, Li, Xu, Du, Qian

“…) convolutional autoencoder (3D-CAE). The proposed 3D-CAE consists of 3D or elementwise operations only, such as 3D convolution, 3D pooling, and 3D batch normalization, to maximally explore spatial-spectral structure information for feature extraction…”
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Improving brain MRI denoising using convolutional AutoEncoder and sparse representations Autor Velayudham, A, Madhan Kumar, K., Krishna Priya, MS

“… However, noise often degrades image quality, leading to inaccurate diagnoses. To address this issue, a Convolutional AutoEncoder-based Orthogonal Matching Pursuit (CAE-OMP…”
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EEG-Based Emotion Classification Using a Deep Neural Network and Sparse Autoencoder Autor Liu, Junxiu, Wu, Guopei, Luo, Yuling, Qiu, Senhui, Yang, Su, Li, Wei, Bi, Yifei

“…), Sparse Autoencoder (SAE), and Deep Neural Network (DNN) together. In the proposed network, the features extracted by the CNN are first sent to SAE for encoding and decoding…”
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Sparse autoencoder for unsupervised nucleus detection and representation in histopathology images Autor Hou, Le, Nguyen, Vu, Kanevsky, Ariel B., Samaras, Dimitris, Kurc, Tahsin M., Zhao, Tianhao, Gupta, Rajarsi R., Gao, Yi, Chen, Wenjin, Foran, David, Saltz, Joel H.

“…We propose a sparse Convolutional Autoencoder (CAE) for simultaneous nucleus detection and feature extraction in histopathology tissue images…”
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Multi-resolution reconstruction of longitudinal streambed footprints using embedded sparse convolutional autoencoders Autor Yang, Yifan, Tang, Zihao, Shao, Dong, Xu, Zhonghou

“… This study introduces an embedded convolutional autoencoder (CAE) architecture designed for the multi-resolution reconstruction of longitudinal streambed footprints as sparse heatmaps…”
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Static video summarization using multi-CNN with sparse autoencoder and random forest classifier Autor Nair, Madhu S., Mohan, Jesna

“…). The features are extracted using four pre-trained models of CNN. These vectors are fed to Sparse Autoencoder, which outputs a combined representation of the input feature vectors…”
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SV-SAE: Layer-Wise Pruning for Autoencoder Based on Link Contributions Autor Rheey, Joohong, Park, Hyunggon

“… The resulting pruned model is referred to as a Shapley Value-based Sparse AutoEncoder (SV-SAE). Using cooperative game theory, the proposed algorithm models the autoencoder…”
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Investigating Beta-Variational Convolutional Autoencoders for the Unsupervised Classification of Chest Pneumonia Autor Akila, Serag Mohamed, Imanov, Elbrus, Almezhghwi, Khaled

“…The world’s population is increasing and so is the challenge on existing healthcare infrastructure to cope with the growing demand in medical diagnosis and…”
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Unsupervised deep learning approach for network intrusion detection combining convolutional autoencoder and one-class SVM Autor Binbusayyis, Adel, Vaiyapuri, Thavavel

“…With the rapid advancement in network technologies, the need for cybersecurity has gained increasing momentum in recent years. As a primary defense mechanism,…”
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Enhanced fault detection in digital VLSI circuits using convolutional autoencoders Autor Savalam, Chandrasekhar, Medisetti, Sanjay, Korapati, Prasanti

“… A Convolutional Autoencoder (CAE) is employed to extract spatial and structural features from circuit test patterns, effectively reducing dimensionality while preserving fault-related information…”
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Leveraging variant of CAE with sparse convolutional embedding and two-stage application-driven data augmentation for image clustering Autor Liu, Yanming, Liu, Jinglei

“… To achieve this, we propose a variant of the convolutional autoencoder (CAE) called SCDAC, which incorporates sparse convolutional embedding and a two-stage application-driven data augmentation approach…”
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Out-of-Roundness Wheel Damage Identification in Railway Vehicles Using AutoEncoder Models Autor Melo, Renato, Finotti, Rafaelle, Guedes, António, Gonçalves, Vítor, Meixedo, Andreia, Ribeiro, Diogo, Barbosa, Flávio, Cury, Alexandre

“…), Sparse AutoEncoder (SAE), and Convolutional AutoEncoder (CAE)—to detect and quantify structural anomalies in railway vehicle wheels, such as polygonization…”
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Transfer learning based on improved stacked autoencoder for bearing fault diagnosis Autor Luo, Shuyang, Huang, Xufeng, Wang, Yanzhi, Luo, Rongmin, Zhou, Qi

“… Stacked autoencoder (SAE) has been widely employed in deep transfer learning research since it is a semi-supervised algorithm…”
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Temperature scaling unmixing framework based on convolutional autoencoder Autor Xu, Jin, Xu, Mingming, Liu, Shanwei, Sheng, Hui, Yang, Zhiru

“…•The framework is a new spatial level constraint method and can be transferred to other convolutional autoencoder-based methods…”
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Structural Damage Identification Using Autoencoders: A Comparative Study Autor Spínola Neto, Marcos, Finotti, Rafaelle, Barbosa, Flávio, Cury, Alexandre

“… Autoencoders, as unsupervised learning models, offer promise for SHM by learning data features and reducing dimensionality…”
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Deep Learning Augmented Data Assimilation: Reconstructing Missing Information with Convolutional Autoencoders Autor Wang, Yueya, Shi, Xiaoming, Lei, Lili, Fung, Jimmy Chi-Hung

“… By training a convolutional autoencoder (CAE) with a long simulation at a coarse “forecast” resolution (T63), we obtained a deep learning approximation…”
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Representation learning via an integrated autoencoder for unsupervised domain adaptation Autor ZHU, Yi, WU, Xindong, QIANG, Jipeng, YUAN, Yunhao, LI, Yun

“… Recently, deep learning methods based on autoencoder have achieved sound performance in representation learning, and many dual or serial autoencoder-based methods take different characteristics…”
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