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RETRACTED ARTICLE: Cluster and cloud computing framework for scientific metrology in flow control

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This article was retracted on 05 December 2022

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Abstract

Scientific metrology is one evergreen and Omni present field that has been continuously experiencing a great deal of research in developing new measurement benchmarks to cope up with the real time advancement in the current market. This has been greatly aided in recent times with the advent of intelligent computing networks and communication protocols through which there has been a great deal of migration towards cloud based services on a demand basis. The essential feature of cloud is the provision of quality service to the clients. The proposed research paper has taken the metrology of monitoring the flow rate in an industrial piping system towards a boiler as the case study and real time implementation achieved with the help of Labview and MyDAQ environment. The data from this DAQ is interpreted into the cloud network with subset reduction and regrouping based on features using a fuzzy c means clustering approach. The experimentation has been done for varying values of tuning constants in order to maintain constant flow and compared with existing research contributions. The results clearly indicate a fast computation time with low complexity overhead which is achieved with the help of cloud distribution.

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Authors and Affiliations

  1. School of Sciences, Qingdao University of Technology, Qingdao, China

    Yumin Wang

  2. Department of Management Science and Engineering, Business School, Qingdao University, Qingdao, China

    Jiangbo Li

  3. Cornell University, Ithaca, USA

    Harry Haoxiang Wang

  4. GoPerception Laboratory, New York, USA

    Harry Haoxiang Wang

Authors
  1. Yumin Wang
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  2. Jiangbo Li
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  3. Harry Haoxiang Wang
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Correspondence to Jiangbo Li.

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This article has been retracted. Please see the retraction notice for more detail:https://doi.org/10.1007/s10586-022-03887-7

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Wang, Y., Li, J. & Wang, H.H. RETRACTED ARTICLE: Cluster and cloud computing framework for scientific metrology in flow control. Cluster Comput 22 (Suppl 1), 1189–1198 (2019). https://doi.org/10.1007/s10586-017-1199-3

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  • DOI: https://doi.org/10.1007/s10586-017-1199-3

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