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A Novel on Transmission Line Tower Big Data Analysis Model Using Altered K-means and ADQL

Author

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  • Se-Hoon Jung

    (School of Connection Major (Bigdata Convergence), Youngsan University of Yangsan, Yangsan 50501, Korea)

  • Jun-Ho Huh

    (Department of Software, Catholic University of Pusan, Busan 46252, Korea)

Abstract

This study sought to propose a big data analysis and prediction model for transmission line tower outliers to assess when something is wrong with transmission line tower big data based on deep reinforcement learning. The model enables choosing automatic cluster K values based on non-labeled sensor big data. It also allows measuring the distance of action between data inside a cluster with the Q-value representing network output in the altered transmission line tower big data clustering algorithm containing transmission line tower outliers and old Deep Q Network. Specifically, this study performed principal component analysis to categorize transmission line tower data and proposed an automatic initial central point approach through standard normal distribution. It also proposed the A-Deep Q-Learning algorithm altered from the deep Q-Learning algorithm to explore policies based on the experiences of clustered data learning. It can be used to perform transmission line tower outlier data learning based on the distance of data within a cluster. The performance evaluation results show that the proposed model recorded an approximately 2.29%~4.19% higher prediction rate and around 0.8% ~ 4.3% higher accuracy rate compared to the old transmission line tower big data analysis model.

Suggested Citation

  • Se-Hoon Jung & Jun-Ho Huh, 2019. "A Novel on Transmission Line Tower Big Data Analysis Model Using Altered K-means and ADQL," Sustainability, MDPI, vol. 11(13), pages 1-25, June.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:13:p:3499-:d:242985
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    References listed on IDEAS

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    Cited by:

    1. Patrycjusz Zarębski & Dominika Zwęglińska-Gałecka, 2020. "Mapping the Food Festivals and Sustainable Capitals: Evidence from Poland," Sustainability, MDPI, vol. 12(24), pages 1-23, December.
    2. Ola G. El‐Taliawi & Nihit Goyal & Michael Howlett, 2021. "Holding out the promise of Lasswell's dream: Big data analytics in public policy research and teaching," Review of Policy Research, Policy Studies Organization, vol. 38(6), pages 640-660, November.
    3. Jianfeng Yao & Guohui Shen & Zhibin Tu & Yong Chen & Wenjuan Lou, 2022. "Wind Tunnel Tests of an Aeroelastic Model of a Long-Span Transmission Tower," Sustainability, MDPI, vol. 14(18), pages 1-15, September.
    4. Isaac Machorro-Cano & Giner Alor-Hernández & Mario Andrés Paredes-Valverde & Lisbeth Rodríguez-Mazahua & José Luis Sánchez-Cervantes & José Oscar Olmedo-Aguirre, 2020. "HEMS-IoT: A Big Data and Machine Learning-Based Smart Home System for Energy Saving," Energies, MDPI, vol. 13(5), pages 1-24, March.

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