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An Improved Optimization Algorithm Based on Density Grid for Green Storage Monitoring System

Author

Listed:
  • Yanting Zhang

    (School of Marxism Studies, Jilin University, Changchun 130021, China)

  • Zhe Zhu

    (School of Marxism Studies, Jilin University, Changchun 130021, China)

  • Wei Ning

    (Economic Research Institute of Jilin Province Development and Reform Commission, Changchun 130061, China)

  • Amir M. Fathollahi-Fard

    (Department of Electrical Engineering, École de Technologie Supérieure, University of Québec, Montréal, QC H3C 1K3, Canada)

Abstract

This study takes a sample of green storage monitoring data for corn from a biochemical energy enterprise, based on the enterprise’s original storage monitoring system while establishing a “green fortress” intending to achieve green and sustainable grain storage. This paper proposes a set of processing algorithms for real-time flow data from the storage system based on cluster analysis to detect abnormal storage conditions, achieve the goal of green grain storage and maximize benefits for the enterprises. Firstly, data from the corn storage monitoring system and the current status of research on data processing algorithms are analyzed. Our study summarizes the processing of re-al-time stream data together with the characteristics of the monitoring system and discusses the application of clustering analysis algorithms. The study includes an in-depth study of the green storage monitoring system data for corn and the processing requirements for real-time stream data. As the main novelty of this research, the optimization algorithm model is applied to the green storage monitoring system for maize and is validated. Finally, the processing results for the green storage monitoring data for maize are presented in graphical and textual formats.

Suggested Citation

  • Yanting Zhang & Zhe Zhu & Wei Ning & Amir M. Fathollahi-Fard, 2022. "An Improved Optimization Algorithm Based on Density Grid for Green Storage Monitoring System," Sustainability, MDPI, vol. 14(17), pages 1-19, August.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:17:p:10822-:d:902024
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    References listed on IDEAS

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    1. Felix Bock, 2022. "Hierarchy cost of hierarchical clusterings," Journal of Combinatorial Optimization, Springer, vol. 44(1), pages 617-634, August.
    2. Chandra Naik & Pushparaj D. Shetty, 2022. "FLAG: fuzzy logic augmented game theoretic hybrid hierarchical clustering algorithm for wireless sensor networks," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 79(4), pages 559-571, April.
    3. Narjes Vara & Mahdieh Mirzabeigi & Hajar Sotudeh & Seyed Mostafa Fakhrahmad, 2022. "Application of k-means clustering algorithm to improve effectiveness of the results recommended by journal recommender system," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(6), pages 3237-3252, June.
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    Cited by:

    1. Maryam Moradi & Gabriel J. Assaf, 2023. "Designing and Building an Intelligent Pavement Management System for Urban Road Networks," Sustainability, MDPI, vol. 15(2), pages 1-17, January.

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