Search Results - "denoising autoencoder"

Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification by Lu, Chen, Wang, Zhen-Ya, Qin, Wei-Li, Ma, Jian

“…Effective fault diagnosis has long been a research topic in the prognosis and health management of rotary machinery engineered systems due to the benefits such…”
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Change detection based on deep feature representation and mapping transformation for multi-spatial-resolution remote sensing images by Zhang, Puzhao, Gong, Maoguo, Su, Linzhi, Liu, Jia, Li, Zhizhou

“…Multi-spatial-resolution change detection is a newly proposed issue and it is of great significance in remote sensing, environmental and land use monitoring,…”
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DEVDAN: Deep evolving denoising autoencoder by Ashfahani, Andri, Pratama, Mahardhika, Lughofer, Edwin, Ong, Yew-Soon

“…The Denoising Autoencoder (DAE) enhances the flexibility of data stream method in exploiting unlabeled samples. Nonetheless, the feasibility of DAE for data…”
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RunDAE model: Running denoising autoencoder models for denoising ECG signals by Samann, Fars, Schanze, Thomas

“…The denoising autoencoder (DAE) is commonly used to denoise bio-signals such as electrocardiogram (ECG) signals through dimensional reduction. Typically, the…”
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Abnormality Monitoring in the Blast Furnace Ironmaking Process Based on Stacked Dynamic Target-Driven Denoising Autoencoders by Jiang, Ke, Jiang, Zhaohui, Xie, Yongfang, Pan, Dong, Gui, Weihua

“…Accurate monitoring of abnormalities is of great significance to the stable operation of the blast furnace ironmaking process. This article proposes a…”
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Deep learning approach for active classification of electrocardiogram signals by Rahhal, M.M. Al, Bazi, Yakoub, AlHichri, Haikel, Alajlan, Naif, Melgani, Farid, Yager, R.R.

“…In this paper, we propose a novel approach based on deep learning for active classification of electrocardiogram (ECG) signals. To this end, we learn a…”
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Lamb wave-based damage imaging of CFRP composite structures using autoencoder and delay-and-sum by Yu, Yinghong, Liu, Xiao, Wang, Yihan, Wang, Yishou, Qing, Xinlin

“…Ultrasonic guided wave is a promising technique for structural health monitoring and nondestructive testing. However, due to the anisotropy and complexity of…”
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An enhancement denoising autoencoder for rolling bearing fault diagnosis by Meng, Zong, Zhan, Xuyang, Li, Jing, Pan, Zuozhou

“…•A novel data preprocessing method is proposed when there is not enough data for the model.•Adjust the regularization parameters appropriately as the number of…”
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Deep Learning-based Integrated Framework for stock price movement prediction by Zhao, Yanli, Yang, Guang

“…Stock market prediction is a very important problem in the economics field. With the development of machine learning, more and more algorithms are applied in…”
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Automatic Fabric Defect Detection with a Multi-Scale Convolutional Denoising Autoencoder Network Model by Mei, Shuang, Wang, Yudan, Wen, Guojun

“…Fabric defect detection is a necessary and essential step of quality control in the textile manufacturing industry. Traditional fabric inspections are usually…”
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Improved deep reinforcement learning for car-following decision-making by Yang, Xiaoxue, Zou, Yajie, Zhang, Hao, Qu, Xiaobo, Chen, Lei

“…Accuracy improvement of Car-following (CF) model has attracted much attention in recent years. Although a few studies incorporate deep reinforcement learning…”
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Wind Turbine Fault Detection Using a Denoising Autoencoder With Temporal Information by Jiang, Guoqian, Xie, Ping, He, Haibo, Yan, Jun

“…Data-driven approaches have gained increasing interests in the fault detection of wind turbines (WTs) due to the difficulty in system modeling and the…”
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Stacked pruning sparse denoising autoencoder based intelligent fault diagnosis of rolling bearings by Zhu, Haiping, Cheng, Jiaxin, Zhang, Cong, Wu, Jun, Shao, Xinyu

“…This paper proposes a new stacked pruning sparse denoising autoencoder (sPSDAE) model for intelligent fault diagnosis of rolling bearings. Different from the…”
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Bidirectional Denoising Autoencoders Based Robust Representation Learning for Underwater Acoustic Target Signal Denoising by Dong, Yafen, Shen, Xiaohong, Wang, Haiyan

“…The marine environmental noise formed by wind noise, rain noise, biological noise, sea surface waves, seismic disturbances, and so on is a kind of interference…”
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A novel Enhanced Collaborative Autoencoder with knowledge distillation for top-N recommender systems by Pan, Yiteng, He, Fazhi, Yu, Haiping

“…In most recommender systems, the data of user feedbacks are usually represented with a set of discrete values, which are difficult to exactly describe users’…”
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An Integrated Multitasking Intelligent Bearing Fault Diagnosis Scheme Based on Representation Learning Under Imbalanced Sample Condition by Zhang, Jiusi, Zhang, Ke, An, Yiyao, Luo, Hao, Yin, Shen

“…Accurate bearing fault diagnosis is of great significance of the safety and reliability of rotary mechanical system. In practice, the sample proportion between…”
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Background Prior-Based Salient Object Detection via Deep Reconstruction Residual by Junwei Han, Dingwen Zhang, Xintao Hu, Lei Guo, Jinchang Ren, Feng Wu

“…Detection of salient objects from images is gaining increasing research interest in recent years as it can substantially facilitate a wide range of…”
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Soft Sensing for Time Series With Irregular Sampling Internals Based on a Denoising Interval Attention LSTM Network by He, Yuchen, Yang, Xueqin, Qian, Lijuan, Yao, Le, Ye, Lingjian, Wu, Ping, Ye, Gangyue, Ye, Weirong, Shen, Yafang

“…The prediction of key quality variables plays an important role in industrial status identification and monitoring. Due to process disturbance and hard device…”
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A Deep Convolutional Coupling Network for Change Detection Based on Heterogeneous Optical and Radar Images by Liu, Jia, Gong, Maoguo, Qin, Kai, Zhang, Puzhao

“…We propose an unsupervised deep convolutional coupling network for change detection based on two heterogeneous images acquired by optical sensors and radars on…”
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State of charge estimation of lithium-ion battery using denoising autoencoder and gated recurrent unit recurrent neural network by Chen, Junxiong, Feng, Xiong, Jiang, Lin, Zhu, Qiao

“…To reduce the influence of the measurement data noise on state of charge (SOC) estimation, a novel neural network method is proposed by combining an input data…”
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