Search Results - Stacked denoising autoencoder~

Automated feature learning for nonlinear process monitoring – An approach using stacked denoising autoencoder and k-nearest neighbor rule by Zhang, Zehan, Jiang, Teng, Li, Shuanghong, Yang, Yupu

“…•Automated feature learning based on stacked denoising autoencoder (SDAE) and k-nearest neighbor rule (kNN…”
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Roller bearing fault diagnosis using stacked denoising autoencoder in deep learning and Gath–Geva clustering algorithm without principal component analysis and data label by Xu, Fan, Tse, Wai tai Peter, Tse, Yiu Lun

“…Most deep learning models such as stacked autoencoder (SAE) and stacked denoising autoencoder (SDAE…”
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Stacked denoising autoencoder based fault location in voltage source converters‐high voltage direct current by Luo, Guomin, Cheng, Mengxiao, Hei, Jiaxin, Wang, Xiaojun, Huang, Weibo, He, Jinghan

“… A stacked denoising autoencoder based fault location method for high voltage direct current transmission systems is proposed…”
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Knock-Knock: Acoustic object recognition by using stacked denoising autoencoders by Luo, Shan, Zhu, Leqi, Althoefer, Kaspar, Liu, Hongbin

“… In this paper, stacked denoising autoencoders are applied to train a deep learning model. Knocking each object in our test set 120 times with a marker pen to obtain…”
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Deep learning for electrolysis process anode effect prediction based on long short-term memory network and stacked denoising autoencoder by Yin, Gang, Li, Yi-Hui, Yan, Fei-Ya, Quan, Peng-Cheng, Wang, Min, Cao, Wen-Qi, Xu, Heng-Quan, Lu, Jian, He, Wen

“… A stacked denoising autoencoder is utilized to denoise and extract features from a large number of long-period parameter data…”
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Stacked denoising autoencoder‐based feature learning for out‐of‐control source recognition in multivariate manufacturing process by Yu, Jianbo, Zheng, Xiaoyun, Wang, Shijin

“… This paper presents an effective and reliable deep learning method known as stacked denoising autoencoder (SDAE…”
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Deep feature fusion‐based stacked denoising autoencoder for tag recommendation systems by Fei, Zhengshun, Wang, Jinglong, Liu, Kangling, Attahi, Eric, Huang, Bingqiang

“…With the rapid development of artificial intelligence technology, commercial robots have gradually entered our daily lives. In order to promote product…”
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Denoising stacked autoencoders‐based near‐infrared quality monitoring method via robust samples evaluation by Lv, Jiapeng, Chen, Zihao, Luan, Xiaoli, Liu, Fei

“…This paper proposes a denoising stacked autoencoders‐based near‐infrared spectroscopy…”
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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

“… improvements, and economical efficiency. This paper investigates an effective and reliable deep learning method known as stacked denoising autoencoder (SDA…”
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Automatic Diagnosis of Microgrid Networks’ Power Device Faults Based on Stacked Denoising Autoencoders and Adaptive Affinity Propagation Clustering by Fan, Bo, Zhang, Xiaodi, Shu, Xin, Xu, Fan

“…This paper presents a model based on stacked denoising autoencoders (SDAEs) in deep learning and adaptive affinity propagation (adAP…”
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Denoising stacked autoencoders for transient electromagnetic signal denoising by Lin, Fanqiang, Chen, Kecheng, Wang, Xuben, Cao, Hui, Chen, Danlei, Chen, Fanzeng

“… of the characteristics of the SFS to denoise the SFS. We introduce the SFSDSA (secondary field signal denoising stacked autoencoders…”
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Short-Term Electricity Price Forecasting With Stacked Denoising Autoencoders by Wang, Long, Zhang, Zijun, Chen, Jieqiu

“… A stacked denoising autoencoder (SDA) model, a class of deep neural networks, and its extended version are utilized to forecast the electricity price hourly…”
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A sparse stacked denoising autoencoder with optimized transfer learning applied to the fault diagnosis of rolling bearings by Sun, Meidi, Wang, Hui, Liu, Ping, Huang, Shoudao, Fan, Peng

“…•Fault diagnosis by stacked autoencoder requires less professional knowledge.•Domain adaptation by optimized transfer learning reduces the algorithm’s complexity…”
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Remote Sensing Image Classification Based on Stacked Denoising Autoencoder by Liang, Peng, Shi, Wenzhong, Zhang, Xiaokang

“… is proposed, which is based on Stacked Denoising Autoencoder. First, the deep network model is built through the stacked layers of Denoising Autoencoder…”
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Probabilistic Stacked Denoising Autoencoder for Power System Transient Stability Prediction With Wind Farms by Su, Tong, Liu, Youbo, Zhao, Junbo, Liu, Junyong

“…To address the uncertainties of renewable energy and loads in transient stability assessment with credible contingencies, this letter proposes a stacked denoising autoencoder (SDAE…”
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Life prediction of lithium-ion batteries based on stacked denoising autoencoders by Xu, Fan, Yang, Fangfang, Fei, Zicheng, Huang, Zhelin, Tsui, Kwok-Leung

“… In this study, a deep learning-based stacked denoising autoencoder (SDAE) method is proposed to directly predict battery life by extracting various battery features…”
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Multimode process monitoring based on hierarchical mode identification and stacked denoising autoencoder by Gao, Huihui, Wei, Chen, Huang, Wenjie, Gao, Xuejin

“…•A novel hierarchical mode identification strategy is proposed for multimode process.•An improved density peaks clustering algorithm called LDRSDPC is…”
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Anomaly detection and diagnosis for wind turbines using long short-term memory-based stacked denoising autoencoders and XGBoost by Zhang, Chen, Hu, Di, Yang, Tao

“… An anomaly detection and diagnosis method for wind turbines using long short-term memory-based stacked denoising autoencoders (LSTM-SDAE…”
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Intelligent fault diagnosis approach with unsupervised feature learning by stacked denoising autoencoder by Xia, Min, Li, Teng, Liu, Lizhi, Xu, Lin, de Silva, Clarence W

“…) based on stacked denoising autoencoder. Representative features are learned by applying the denoising autoencoder to the unlabelled data in an unsupervised manner…”
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Intelligent analysis of tool wear state using stacked denoising autoencoder with online sequential-extreme learning machine by Ou, Jiayu, Li, Hongkun, Huang, Gangjin, Yang, Guowei

“… In this research, a new method named stacked…”
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