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Akademický článek

Research on Pump Inspection Cycle Early Warning Method Based on Big Data

Autor: Lihong Du, Yufang Liu, Fei Cao, Fang Li, Guizhi Min, Zhongbo Liu, Zhixue Xia, Zhanmin Zhang

Publikováno v: E3S Web of Conferences, Vol 245, p 02026 (2021)

At present, the existing indicator diagram can only be used for expost judgment and can not give early warning, and the influencing factors of pump inspection period are nonlinear, multi constrained and multi variable. In this paper, big data machine

Externí odkaz: https://doaj.org/article/13ae60a8a9aa42588cb338339c3be6d2

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Conference

Research on Pump Inspection Cycle Early Warning Method Based on Big Data.

Autor: Duan, L., Abdullah, A.Z., Lihong, Du, Yufang, Liu, Fei, Cao, Fang, Li, Guizhi, Min, Zhongbo, Liu, Zhixue, Xia, Zhanmin, Zhang

Publikováno v: E3S Web of Conferences; 4/23/2021, Vol. 252, p1-5, 5p

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DCNN Based Real-Time Adaptive Ship License Plate Recognition (DRASLPR)

Autor: Haoyun Sun, Zhanmin Zhang, Guizhi Min, Jiehan Zhou, Weishan Zhang, Xin Liu

Publikováno v: iThings/GreenCom/CPSCom/SmartData

Ship license plate recognition is challenging due to the diversity of plate locations and text types. This paper proposes a DCNN-based (deep convolutional neural network) online adaptive real-time ship license plate recognition approach, namely, DRAS

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bde79fb7a41f995b51a86661e402eb14
https://doi.org/10.1109/cybermatics_2018.2018.00304

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Fully convolutional network based ship plate recognition

Autor: Weishan Zhang, Haoyun Sun, Jiehan Zhou, Xin Liu, Zhanmin Zhang, Guizhi Min

Publikováno v: SMC

Ship plate recognition is challenging due to variations of plate locations and text types. This paper proposes an effcient Fully Convolutional Network based Plate Recognition approach FCNPR, which uses a CNN (Convolutional Neural Network) to locate s

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::371302c17023a478067dfc1861a1b1be
http://urn.fi/urn:nbn:fi-fe2020042822734

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LSTM-based service migration for pervasive cloud computing

Autor: Weishan Zhang, Zhanmin Zhang, Haifeng Jing, Zhang Yafei, Xin Liu, Guizhi Min, Jiehan Zhou

Publikováno v: iThings/GreenCom/CPSCom/SmartData

Service migration in pervasive cloud computing is important for leveraging cloud resources to execute mobile applications effectively and efficiently. This paper proposes a LSTM (long and short-term memory model) based service migration approach for

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e9fc4643a76c51f1eaeab964a726cae1
http://urn.fi/urn:nbn:fi-fe202003238887

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