Search Results - "deep convolutional autoencoder"

Non-intrusive reduced order modeling of natural convection in porous media using convolutional autoencoders: Comparison with linear subspace techniques by Kadeethum, T., Ballarin, F., Choi, Y., O’Malley, D., Yoon, H., Bouklas, N.

“…Natural convection in porous media is a highly nonlinear multiphysical problem relevant to many engineering applications (e.g., the process of CO2…”
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MR‐DCAE: Manifold regularization‐based deep convolutional autoencoder for unauthorized broadcasting identification by Zheng, Qinghe, Zhao, Penghui, Zhang, Deliang, Wang, Hongjun

“…Nowadays, radio broadcasting plays an important role in people's daily life. However, unauthorized broadcasting stations may seriously interfere with normal…”
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Convolutional Autoencoder-Based Damage Detection for Urban Railway Tracks Using an Ultra-Weak FBG Array Monitoring System by Chen, Jiahui, Li, Qiuyi, Zhang, Shijie, Lin, Chao, Wei, Shiyin

“…Urban railway tracks inevitably suffer damage due to the cyclic load of trains, necessitating structural health monitoring (SHM) with the primary purpose of…”
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Robust deep fuzzy K-means clustering for image data by Wu, Xiaoling, Yu, Yu-Feng, Chen, Long, Ding, Weiping, Wang, Yingxu

“…Image clustering is a difficult task with important application value in computer vision. The key to this task is the quality of images features. Most of…”
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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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One-dimensional deep convolutional autoencoder active infrared thermography: Enhanced visualization of internal defects in FRP composites by Zhang, Yubin, Xu, Changhang, Liu, Pengqian, Xie, Jing, Han, Yage, Liu, Rui, Chen, Lina

“…Fiber-reinforced polymer (FRP) composites have been widely applied in different industrial fields, thereby necessitating the employment of non-destructive…”
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RDMAA: Robust Defense Model against Adversarial Attacks in Deep Learning for Cancer Diagnosis by A. Abd El-Aziz, Atrab, A. El-Khoribi, Reda, Khalifa, Nour Eldeen

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Robustness of facial expression recognition systems in the presence of mild and uniform blur by H N, Naveen Kumar, Patil, Chandrashekar M, M S, Guru Prasad, K R, Sharath, Gujjar J, Praveen

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Unsupervised Seismic Footprint Removal With Physical Prior Augmented Deep Autoencoder by Qian, Feng, Yue, Yuehua, He, Yu, Yu, Hongtao, Zhou, Yingjie, Tang, Jinliang, Hu, Guangmin

“…Seismic acquisition footprints appear as stably faint and dim structures and emerge fully spatially coherent, causing inevitable damage to useful signals…”
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Seismic random noise suppression using deep convolutional autoencoder neural network by Song, Hui, Gao, Yang, Chen, Wei, Xue, Ya-juan, Zhang, Hua, Zhang, Xiang

“…Due to human or environmental factors, random noise will inevitably be introduced during seismic data acquisition. Contaminated seismic data seriously affect…”
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Broadband Communication Signal Detection Using Deep Convolutional Autoencoder and Spectrum Center Network by Wei, Bin

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Unsupervised Erratic Seismic Noise Attenuation With Robust Deep Convolutional Autoencoders by Qian, Feng, Guo, Wei, Liu, Zhangbo, Yu, Hongtao, Zhang, Gulan, Hu, Guangmin

“…Erratic seismic noise, following a (known or unknown) non-Gaussian distribution, poses a formidable challenge to conventional methods of random noise…”
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Deep Convolutional Clustering-Based Time Series Anomaly Detection by Chadha, Gavneet Singh, Islam, Intekhab, Schwung, Andreas, Ding, Steven X.

“…This paper presents a novel approach for anomaly detection in industrial processes. The system solely relies on unlabeled data and employs a 1D-convolutional…”
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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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Convolutional Autoencoder-Based medical image compression using a novel annotated medical X-ray imaging dataset by Fettah, Amina, Menassel, Rafik, Gattal, Abdeljalil, Gattal, Abdelhak

“…•Novel Medical Dataset MXID is a meticulously annotated medical dataset designed specifically for body part classification, gender classification, and image…”
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Unsupervised Intense VSP Coupling Noise Suppression with Iterative Robust Deep Learning by Qian, Feng, Hua, Haowei, Wen, Yuhang, Zong, Jingjing, Zhang, Gulan, Hu, Guangmin

“…Due to the poorly coupled geophones present in boreholes, vertical seismic profiling (VSP) data are known to suffer from intense coupling noise, which causes…”
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PredMaX: Predictive maintenance with explainable deep convolutional autoencoders by Hajgató, Gergely, Wéber, Richárd, Szilágyi, Botond, Tóthpál, Balázs, Gyires-Tóth, Bálint, Hős, Csaba

“…A novel data exploration framework (PredMaX) for predictive maintenance is introduced in the present paper. PredMaX offers automatic time period clustering and…”
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Using Channel State Information for Physical Tamper Attack Detection in OFDM Systems: A Deep Learning Approach by Dehmollaian, Eshagh, Etzlinger, Bernhard, Torres, Nuria Ballber, Springer, Andreas

“…This letter proposes a deep learning approach to detect a change in the antenna orientation of transmitter or receiver as a physical tamper attack in OFDM…”
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Genetic Deep Convolutional Autoencoder Applied for Generative Continuous Arterial Blood Pressure via Photoplethysmography by Sadrawi, Muammar, Lin, Yin-Tsong, Lin, Chien-Hung, Mathunjwa, Bhekumuzi, Fan, Shou-Zen, Abbod, Maysam F., Shieh, Jiann-Shing

“…Hypertension affects a huge number of people around the world. It also has a great contribution to cardiovascular- and renal-related diseases. This study…”
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Deep convolutional autoencoder for urban land use classification using mobile device data by Sun, Zhihao, Peng, Zhenghong, Yu, Yang, Jiao, Hongzan

“…Mobile phone data can provide insightful location-based information on the interactions between individuals and the urban environment, e.g. urban land-use…”
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