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Data compression and prediction using machine learning for industrial IoT
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Publication Year
2018-04-19
Journal
International Conference on Information Networking
Publisher
IEEE Computer Society
Citation
International Conference on Information Networking, Vol.2018-January, pp.818-820
Keyword
Big dataData compressionIndustrial dataMachine learningRegression
Mesh Keyword
Divide and conquer methodsHigh-accuracyIndustrial datumLossy compressionsRegression
All Science Classification Codes (ASJC)
Computer Networks and CommunicationsInformation Systems
Abstract
Industrial IoT generates big data that is useful for getting insight from data analysis but storing all the data is a burden. To resolve it, we propose to compress the industrial data using neural network regression into a representative vector with lossy compression. For efficiency of the compression, we use the divide-and-conquer method such that the industrial data can be handled by the chunk size of data. Through our experiments, we verify that industrial data is represented by a function and predicted with high accuracy.
ISSN
1976-7684
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/36283
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85047000578&origin=inward
DOI
https://doi.org/10.1109/icoin.2018.8343232
Journal URL
http://www.icoin.org/
Type
Conference
Funding
This research was supported by the MSIP (Ministry of Science and ICT), Korea, under the National Program for Excellence in SW) supervised by the IITP (Institute for Information & communications Technology Promotion) (2015- 0-00908).ACKNOWLEDGMENT This research was supported by the MSIP (Ministry of Science and ICT), Korea, under the National Program for Excellence in SW) supervised by the IITP (Institute for Information & communications Technology Promotion) (2015-0-0090)8 .
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Choi, Youngjune Image
Choi, Youngjune최영준
Department of Software and Computer Engineering
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