L
Lei Ren
Researcher at Beihang University
Publications - 109
Citations - 4294
Lei Ren is an academic researcher from Beihang University. The author has contributed to research in topics: Cloud manufacturing & Cloud computing. The author has an hindex of 23, co-authored 90 publications receiving 2810 citations. Previous affiliations of Lei Ren include Chinese Ministry of Education & Chinese Academy of Sciences.
Papers
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Cloud manufacturing: a new manufacturing paradigm
Lin Zhang,Yongliang Luo,Fei Tao,Bo Hu Li,Lei Ren,Xuesong Zhang,Hua Guo,Ying Cheng,Anrui Hu,Yongkui Liu +9 more
TL;DR: The concept of CMfg, including its architecture, typical characteristics and the key technologies for implementing aCMfg service platform, is discussed and three core components for constructing a CMfg system, i.e. CMfg resources, manufacturing cloud service and manufacturing cloud are studied.
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Cloud manufacturing: key characteristics and applications
TL;DR: A four-process structure is proposed to describe the typical scenario in cloud manufacturing, hoping to provide a theoretical reference for practical applications and the key characteristics of cloud manufacturing are presented in order to clarify the cloud manufacturing concept.
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Bearing remaining useful life prediction based on deep autoencoder and deep neural networks
TL;DR: A novel eigenvector based on time–frequency-wavelet joint features is proposed to effectively represent bearing degradation process and a deep autoencoder based joint features compression and computing method is presented to retain effective information without increasing the scale of DNN.
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Cloud manufacturing: from concept to practice
TL;DR: A new perspective for cloud manufacturing, as well as a cloud-to-ground solution, including the terminology, MfgCloud, and applications, can push forward this new paradigm from concept to practice.
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Remaining Useful Life Prediction for Lithium-Ion Battery: A Deep Learning Approach
TL;DR: An integrated deep learning approach for RUL prediction of lithium-ion battery by integrating autoencoder with deep neural network (DNN) is proposed and results show that the proposed approach can improve the accuracy of RUL Prediction.