Search Results - "Convolutional denoising autoencoder (CDAE)"

An Unsupervised-Learning-Based Approach for Automated Defect Inspection on Textured Surfaces by Mei, Shuang, Yang, Hua, Yin, Zhouping

“…Automated defect inspection has long been a challenging task especially in industrial applications, where collecting and labeling large amounts of defective…”
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Recognition and Localization of FBG Temperature Sensing Based on Combined CDAE and 1-DCNN by Jiang, Hong, Tang, Rui, Wang, Chenyang, Zhao, Yihan, Li, Hao

“… To address this issue, we construct a sensor network using spectral encoding and propose a novel approach that combines convolutional denoising autoencoder (CDAE…”
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Signal‐to‐noise ratio enhancement for Raman spectra based on optimized Raman spectrometer and convolutional denoising autoencoder by Fan, Xian‐guang, Zeng, Yingjie, Zhi, Yu‐Liang, Nie, Ting, Xu, Ying‐jie, Wang, Xin

“…‐the‐shelf cylindrical lens. On the other side of the algorithm, a relevant automatic denoising method of convolutional denoising autoencoder (CDAE…”
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Terahertz spectra reconstructed using convolutional denoising autoencoder for identification of rice grains infested with Sitophilus oryzae at different growth stages by Pu, Hongbin, Yu, Jingxiao, Luo, Jie, Paliwal, Jitendra, Sun, Da-Wen

“… Then, the convolutional denoising autoencoder (CDAE) was used to reconstruct THz spectra to reduce…”
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Deep Spatio-Temporal Representation and Ensemble Classification for Attention Deficit/Hyperactivity Disorder by Liu, Shuaiqi, Zhao, Ling, Wang, Xu, Xin, Qi, Zhao, Jie, Guttery, David S., Zhang, Yu-Dong

“… We propose an algorithm based on convolutional denoising autoencoder (CDAE) and adaptive boosting decision trees (AdaDT…”
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Leukemia detection and classification using computer-aided diagnosis system with falcon optimization algorithm and deep learning by Asar, Turky Omar, Ragab, Mahmoud

“…Leukemia is a type of blood tumour that occurs because of abnormal enhancement in WBCs (white blood cells) in the bone marrow of the human body. Blood-forming…”
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CCpos: WiFi Fingerprint Indoor Positioning System Based on CDAE-CNN by Qin, Feng, Zuo, Tao, Wang, Xing

“… (CADE-CNN Positioning), which is based on a convolutional denoising autoencoder (CDAE) and a convolutional neural network…”
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Denoising and Baseline Correction Methods for Raman Spectroscopy Based on Convolutional Autoencoder: A Unified Solution by Han, Ming, Dang, Yu, Han, Jianda

“… One is a denoising algorithm that uses a convolutional denoising autoencoder (CDAE model), and the other is a baseline correction algorithm based on a convolutional autoencoder…”
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Detection of debonding defects in honeycomb sandwich composite structures using low-power ultrasound excited thermography optimized by post-processing techniques by Zhang, Yubin, Xu, Changhang, Liu, Pengqian, Liu, Rui, Zhao, Qing, Wang, Longbo, Xie, Jing

“… A new post-processing framework, convolutional denoising autoencoder (CDAE) combined with typical post-processing techniques, is described…”
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Separating the EoR signal with a convolutional denoising autoencoder: a deep-learning-based method by Li, Weitian, Xu, Haiguang, Ma, Zhixian, Zhu, Ruimin, Hu, Dan, Zhu, Zhenghao, Gu, Junhua, Shan, Chenxi, Zhu, Jie, Wu, Xiang-Ping

“… of interferometers, will generate significant fluctuations along the frequency dimension. To address this issue, we propose a novel deep-learning-based method that uses a nine-layer convolutional denoising autoencoder (CDAE…”
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Convolutional Autoencoder-Based Flaw Detection for Steel Wire Ropes by Zhang, Guoyong, Tang, Zhaohui, Zhang, Jin, Gui, Weihua

“… In the context of these issues, this study proposes a new flaw detection method based on the convolutional denoising autoencoder (CDAE…”
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Physical Layer Authentication for Industrial Control Based on Convolutional Denoising Autoencoder by Chen, Yanru, He, Haoyu, Liu, Shengjie, Zhang, Yuanyuan, Li, Yang, Xing, Bin, Guo, Bing, Chen, Liangyin

“… To address these challenges, we propose a convolutional denoising autoencoder (CDAE) model that reduces feature dimensions, eliminates noise, and extracts key vectors…”
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Dynamic mechanical response prediction model of honeycomb structure based on machine learning method and finite element method by Shen, Xingyu, Hu, Qianran, Zhu, Difeng, Qi, Shaobo, Huang, Chunyang, Yuan, Mengqi, Qian, Xinming, Guo, Ran

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Assessment of flame stability through a convolutional denoising autoencoder and statistical analysis by Han, Zhezhe, Tang, Xiaoyu, Hossain, Md. Moinul, Xu, Chuanlong

“… In this method, the deep image features are extracted by an unsupervised convolutional denoising autoencoder (CDAE…”
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A novel dimensionality reduction approach for ECG signal via convolutional denoising autoencoder with LSTM by Dasan, Evangelin, Panneerselvam, Ithayarani

“…•Compressing the signal before transmission can reduce the signal transmission cost in wearable technology.•Reduced transmission time can increase the battery…”
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Radar Signal Intra-Pulse Modulation Recognition Based on Convolutional Denoising Autoencoder and Deep Convolutional Neural Network by Qu, Zhiyu, Wang, Wenyang, Hou, Changbo, Hou, Chenfan

“… A radar signal intra-pulse modulation recognition method based on convolutional denoising autoencoder (CDAE…”
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Performance evaluation of image denoising developed using convolutional denoising autoencoders in chest radiography by Lee, Donghoon, Choi, Sunghoon, Kim, Hee-Joung

“… In this study, we introduce an image denoising technique based on a convolutional denoising autoencoder (CDAE…”
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A novel filtering method for geodetically determined ocean surface currents using deep learning by Gibbs, Laura, Bingham, Rory J., Paiement, Adeline

“…Determining an accurate picture of ocean currents is an important societal challenge for oceanographers, aiding our understanding of the vital role currents…”
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A comprehensive review on encoder–decoder architectures in ECG signal compression and denoising: opportunities, challenges, and prospects by Das, Maumita, Sahana, Bikash Chandra

“…Purpose Cardiovascular disease diagnosis relies on the accurate interpretation of electrocardiogram (ECG) signals, which are sensitive to noise and artifacts…”
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The recognition of multi-components signals based on semantic segmentation by Hou, Changbo, Fu, Dingyi, Hua, Lijie, Lin, Yun, Liu, Guowei, Zhou, Zhichao

“…). A convolutional denoising autoencoder (CDAE) is established to filter the TFIs. Three semantic segmentation networks are used, a fully convolutional neural network (FCN-8s…”
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