Search Results - "convolutional autoencoder (CAE)"

Commonality Autoencoder: Learning Common Features for Change Detection From Heterogeneous Images by Wu, Yue, Li, Jiaheng, Yuan, Yongzhe, Qin, A. K., Miao, Qi-Guang, Gong, Mao-Guo

“…Change detection based on heterogeneous images, such as optical images and synthetic aperture radar images, is a challenging problem because of their huge…”
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Deep convolutional autoencoder for radar-based classification of similar aided and unaided human activities by Seyfioglu, Mehmet Saygin, Ozbayoglu, Ahmet Murat, Gurbuz, Sevgi Zubeyde

“…Radar-based activity recognition is a problem that has been of great interest due to applications such as border control and security, pedestrian…”
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One-Dimensional Residual Convolutional Autoencoder Based Feature Learning for Gearbox Fault Diagnosis by Yu, Jianbo, Zhou, Xingkang

“…Vibration signals are generally utilized for machinery fault diagnosis to perform timely maintenance and then reduce losses. Thus, the feature extraction on…”
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A Particle Swarm Optimization-Based Flexible Convolutional Autoencoder for Image Classification by Sun, Yanan, Xue, Bing, Zhang, Mengjie, Yen, Gary G.

“…Convolutional autoencoders (CAEs) have shown their remarkable performance in stacking to deep convolutional neural networks (CNNs) for classifying image data…”
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An anomaly detection method for gas turbines based on single-condition training with zero-fault sample by Yue, Yubin, Wang, Hongjun, Zhang, Peishuo, Gu, Fengshou

“…Enhancing anomaly detection performance is essential for effective gas turbine condition monitoring and health maintenance. However, in industrial…”
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FedRUL: A New Federated Learning Method for Edge-Cloud Collaboration Based Remaining Useful Life Prediction of Machines by Guo, Liang, Yu, Yaoxiang, Qian, Mengui, Zhang, Ruiqi, Gao, Hongli, Cheng, Zhe

“…In real industrial applications, intelligent methods are recently emerging for remaining useful life (RUL) prediction. However, their development is hindered…”
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Machine learning-based reduced-order reconstruction method for flow fields by Gao, Hu, Qian, Weixin, Dong, Jiankai, Liu, Jing

“…•Design of the ROR model framework based on partial differential operators.•Extraction of low-dimensional flow field features using an autoencoder.•Combination…”
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Intelligent framework for unsupervised damage detection in bridges using deep convolutional autoencoder with wavelet transmissibility pattern spectra by Li, Shuai, Cao, Yuxi, Gdoutos, Emmanuel E., Tao, Mei, Faisal Alkayem, Nizar, Avci, Onur, Cao, Maosen

“…Deep Learning has been increasingly utilized in structural damage detection. Existing relevant studies often highlight the benefits of supervised deep learning…”
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A Convolutional Autoencoder Method for Simultaneous Seismic Data Reconstruction and Denoising by Jiang, Jinsheng, Ren, Haoran, Zhang, Meng

“…Petroleum geophysical exploration is based on seismic data and has been widely affected by deep learning technology in recent years. As a consequence of the…”
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A convolutional autoencoder-based method for learning and ranking personal daylighting preference by Mah, Dongjun, Tzempelikos, Athanasios

“…•Novel method for leaning personal binary daylighting preference.•Two-stage training method: feature extraction module and preference inference…”
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Sky Image Prediction Model Based on Convolutional Auto-Encoder for Minutely Solar PV Power Forecasting by Fu, Yuwei, Chai, Hua, Zhen, Zhao, Wang, Fei, Xu, Xunjian, Li, Kangping, Shafie-Khah, Miadreza, Dehghanian, Payman, Catalao, Joao P. S.

“…The precise minute time scale forecasting of an individual PV power station output relies on accurate prediction of cloud distribution, which can lead to…”
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Convolutional Autoencoder Model for Finger-Vein Verification by Hou, Borui, Yan, Ruqiang

“…This paper presents a novel deep learning-based method that integrates a Convolutional Auto-Encoder (CAE) with support vector machine (SVM) for finger-vein…”
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Unsupervised Change Detection Using Convolutional-Autoencoder Multiresolution Features by Bergamasco, Luca, Saha, Sudipan, Bovolo, Francesca, Bruzzone, Lorenzo

“…The use of deep learning (DL) methods for change detection (CD) is currently dominated by supervised models that require a large number of labeled samples…”
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CAE-MAS: Convolutional Autoencoder Interference Cancellation for Multiperson Activity Sensing With FMCW Microwave Radar by Raeis, Hossein, Kazemi, Mohammad, Shirmohammadi, Shervin

“…Human activity sensing is a crucial component of health monitoring and smart environment applications. Frequency-modulated continuous-wave (FMCW) radars can be…”
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Unsupervised Hyperspectral Image Band Selection Based on Deep Subspace Clustering by Zeng, Meng, Cai, Yaoming, Cai, Zhihua, Liu, Xiaobo, Hu, Peng, Ku, Junhua

“…Hyperspectral image (HSI) consists of hundreds of continuous narrow bands with high redundancy, resulting in the curse of dimensionality and an increased…”
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Nonlinear reduced-order modeling for three-dimensional turbulent flow by large-scale machine learning by Ando, Kazuto, Onishi, Keiji, Bale, Rahul, Kuroda, Akiyoshi, Tsubokura, Makoto

“…A large-scale machine learning-based nonlinear reduced-order modeling method was developed for a three-dimensional turbulent flow field (Re=1000) using a…”
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MSSL: Hyperspectral and Panchromatic Images Fusion via Multiresolution Spatial-Spectral Feature Learning Networks by Qu, Jiahui, Shi, Yanzi, Xie, Weiying, Li, Yunsong, Wu, Xianyun, Du, Qian

“…The fusion of hyperspectral (HS) and panchromatic (PAN) images aims to generate a fused HS image that combines spectral information of the HS image with…”
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Unsupervised VSP up- and downgoing wavefield separation via dual convolutional autoencoders by Lu, Cai, Mu, Zuochen, Zong, Jingjing, Wang, Tengyu

“…Vertical seismic profiling (VSP) is widely applied in the field of seismic exploration to deliver high-quality subsurface images and enable quantitative…”
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A machine learning approach to assess magnitude of asynchrony breathing by Loo, N.L., Chiew, Y.S., Tan, C.P., Mat-Nor, M.B., Ralib, A.M.

“…•Magnitude of asynchrony breathing assessment during MV is underrecognized.•A machine learning approach is presented to reconstruct asynchrony breathing.•Model…”
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Electrocardiogram Signal Compression Using Deep Convolutional Autoencoder with Constant Error and Flexible Compression Rate by Bekiryazıcı, Tahir, Aydemir, Gürkan, Gürkan, Hakan

“…Objectives Electrocardiogram (ECG) signals are beneficial for diagnosing cardiac diseases. The cardiac patients' life quality likely increases with continuous…”
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