Search Results - Stacked denoising convolution autoencoder

Temporal denoising and deep feature learning for enhanced defect detection in thermography using stacked denoising convolution autoencoder by Yerneni, Naga Prasanthi, Ghali, V.S., Vesala, G.T., Wang, Fei, Mulaveesala, Ravibabu

“…•Temporal denoising of thermal profiles along with deep feature learning for enhanced defect detection in FMTWI is highlighted through a stacked denoising convolution autoencoder…”
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Anomaly Detection Through Deep Feature Extraction for Automatic Defect Detection in Quadratic Frequency Modulated Thermal Wave Imaging by Yerneni, Naga Prasanthi, Ghali, V. S., Swapna, M. N., Vesala, G. T.

“… The proposed model utilizes the pretrained stacked denoising convolution autoencoder (SDCAE) to extract deep features and feed them to a local outlier factor (LOF…”
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Robust Feature Extraction for Geochemical Anomaly Recognition Using a Stacked Convolutional Denoising Autoencoder by Xiong, Yihui, Zuo, Renguang

“… Our approach adopted a stacked convolutional denoising autoencoder (SCDAE) to extract robust features and decreased the level of sensitivity to partially corrupted data, that is, input data that are partially missing…”
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Reduced Biquaternion Stacked Denoising Convolutional AutoEncoder for RGB-D Image Classification by Huang, Xiang, Gai, Shan

“… To address these problems, this letter proposes a novel RGB-D image classification framework based on reduced biquaternion stacked denoising convolutional autoencoder (RQ-SDCAE…”
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Elimination of Random Mixed Noise in ECG Using Convolutional Denoising Autoencoder With Transformer Encoder by Chen, Meng, Li, Yongjian, Zhang, Liting, Liu, Lei, Han, Baokun, Shi, Wenzhuo, Wei, Shoushui

“… To suppress random mixed noise (RMN) in ECG with less distortion, we propose a Transformer-based Convolutional Denoising AutoEncoder model (TCDAE) in this study…”
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Intelligent fault diagnosis method of rolling bearing based on stacked denoising autoencoder and convolutional neural network by Che, Changchang, Wang, Huawei, Ni, Xiaomei, Fu, Qiang

“… To solve those problems, an intelligent fault diagnosis model based on stacked denoising autoencoder (SDAE…”
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Gaussian Mixture with Max Expectation Guide for Stacked Architecture of Denoising Autoencoder and DRBM for Medical Chest Scans and Disease Identification by Jamjoom, Mona, Mahmoud, Abeer M., Abbas, Safia, Hodhod, Rania

“…) to extract the regions of interest (ROI), while a convolutional denoising autoencoder (DAE) and deep restricted Boltzmann machine…”
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A Hierarchical Intrusion Detection Model Combining Multiple Deep Learning Models With Attention Mechanism by Xu, Hongsheng, Sun, Libo, Fan, Ganglong, Li, Wanxing, Kuang, Guofang

“…In order to ensure the security of computer systems and networks, it is very important to design and implement intrusion detection systems that can detect and…”
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One-Dimensional Residual Convolutional Autoencoder Based Feature Learning for Gearbox Fault Diagnosis by Yu, Jianbo, Zhou, Xingkang

“… In this article, a new DNN, one-dimensional residual convolutional autoencoder (1-DRCAE), is proposed for learning features from vibration signals directly in an unsupervised-learning way…”
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Single channel audio source separation using convolutional denoising autoencoders by Grais, Emad M., Plumbley, Mark D.

“… In this work, we propose to use deep fully convolutional denoising autoencoders (CDAEs) for monaural audio source separation…”
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Enhancing Human Activity Recognition with IMU-based Motion Analysis by Singh, Arth, Verma, Aditya, Cherukuri, Babitha

“…In this research, we delve into the domain of Human Activity Recognition (HAR) using deep learning techniques applied to data collected from a single wearable…”
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Rolling Bearing Fault Diagnosis Method Based on Stacked Denoising Autoencoder and Convolutional Neural Network by Wang, Yumin, Han, Minghong, Liu, Wei

“… A fault diagnosis method towards non-stationary signal is proposed in this paper. A fault diagnosis model of combining stacked denoising autoencoder (SDAE…”
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Stacked Convolutional Denoising Auto-Encoders for Feature Representation by Du, Bo, Xiong, Wei, Wu, Jia, Zhang, Lefei, Zhang, Liangpei, Tao, Dacheng

“… To solve this problem, this paper proposes an unsupervised deep network, called the stacked convolutional denoising auto-encoders, which can map images to hierarchical representations without any label information…”
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Deep Learning-Based Machinery Fault Diagnostics

“…This book offers a compilation for experts, scholars, and researchers to present the most recent advancements, from theoretical methods to the applications of…”
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Deep Fault Recognizer: An Integrated Model to Denoise and Extract Features for Fault Diagnosis in Rotating Machinery by Guo, Xiaojie, Shen, Changqing, Chen, Liang

“…) and deep convolution neural network (DCNN), have been developed with satisfactory performances to conduct machinery fault diagnosis…”
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The Analysis about Compressed Sensing Reconstruction Algorithm Based on Machine Learning Applied in Interference Multispectral Images by Han, Chang

“… In order to make up for the shortcomings of traditional compression and reconstruction algorithms, the stacked convolution denoising autoencoder (SCDA…”
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A Comparative Study of Object Tracking using CNN and SDAE by Yang, Wei, Wang, Wei, Gao, Yang, Jin, Zhanpeng

“…) and the stacked denoising autoencoder (SDAE), as opposed to the most frequently used tracking algorithms that only learn the appearance of the tracked object…”
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EIT-CDAE: A 2-D Electrical Impedance Tomography Image Reconstruction Method Based on Auto Encoder Technique by Gao, Yue, Lu, Yewangqing, Li, Hui, Liu, Boxiao, Li, Yongfu, Chen, Mingyi, Wang, Guoxing, Lian, Yong

“… Therefore, in this work, we have proposed a new EIT image reconstruction algorithm based on the convolution denoising autoencoder (CDAE…”
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Control System Response Improvement via Denoising Using Deep Neural Networks by Fathi, Kiavash, Mahdavi, Mehdi

“…Noise is an inseparable part of control systems. Every sensor reading used for determining the state of a control system is corrupted with noise., therefor…”
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Holistic Approaches to Music Genre Classification using Efficient Transfer and Deep Learning Techniques by Prabhakar, Sunil Kumar, Lee, Seong-Whan

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