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Multimedia Technology and Enhanced Learning. Second EAI International Conference, ICMTEL 2020, Leicester, UK, April 10-11, 2020, Proceedings, Part II

Research Article

Big Data Fast Extraction Method of Lithium Ion Screen Exchange Feature in Cloud Computing

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  • @INPROCEEDINGS{10.1007/978-3-030-51103-6_3,
        author={Xiang Xiao and Zhuan Wei and Pei Pei},
        title={Big Data Fast Extraction Method of Lithium Ion Screen Exchange Feature in Cloud Computing},
        proceedings={Multimedia Technology and Enhanced Learning. Second EAI International Conference, ICMTEL 2020, Leicester, UK, April 10-11, 2020, Proceedings, Part II},
        proceedings_a={ICMTEL PART 2},
        year={2020},
        month={7},
        keywords={Cloud computing Lithium ion screen Exchange Feature big data Rapid extraction},
        doi={10.1007/978-3-030-51103-6_3}
    }
    
  • Xiang Xiao
    Zhuan Wei
    Pei Pei
    Year: 2020
    Big Data Fast Extraction Method of Lithium Ion Screen Exchange Feature in Cloud Computing
    ICMTEL PART 2
    Springer
    DOI: 10.1007/978-3-030-51103-6_3
Xiang Xiao1,*, Zhuan Wei1, Pei Pei1
  • 1: Department of Information Science and Engineering
*Contact email: xiaoxiang665366@163.com

Abstract

The characteristic distribution performance of big data, the exchange characteristic of lithium ion screen in cloud computing environment, quantitatively reflects the running state of lithium ion screen exchanger, in order to realize the effective monitoring of lithium ion screen exchange process. A fast extraction algorithm of Li-ion screen exchange feature big data based on big data is proposed. Big data acquisition of lithium ion screen exchange characteristics is realized in lithium ion screen exchange array, and the statistical analysis model of big data mining is constructed. In big data distribution subspace, the spectral feature extraction method is used to extract the spectral stripe feature of Li-ion screen exchange feature big data, and the extracted spectral stripe feature is fuzzy clustering and mining by adaptive neural network learning algorithm. Big data rapid extraction of exchange characteristics of lithium ion screen was realized. The simulation results show that the method has high accuracy in fast extraction of exchange features of lithium ion screen, strong resolution of exchange characteristics of lithium ion screen, and has good application value in high precision measurement of exchange characteristics of lithium ion screen.

Keywords
Cloud computing Lithium ion screen Exchange Feature big data Rapid extraction
Published
2020-07-19
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-030-51103-6_3
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