International Journal of Internet, Broadcasting and Communication

  • 제16권2호
  • /
  • Pages.203-208
  • /
  • 2024
  • /
  • 2288-4920(pISSN)
  • /
  • 2288-4939(eISSN)
한국인터넷방송통신학회 (The Institute of Internet, Broadcasting and Communication)
권호 표지
DOI QR코드

DOI QR Code

A Quantitative Review on Deep Learning and Smart Factory from 2010 to 2023

  • Yong Sauk Hau (Department of Business Administration, Yeungnam University)
  • 투고 : 2024.04.08
  • 심사 : 2024.04.24
  • 발행 : 2024.05.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

The convergence of deep learning and smart factory is drawing a lot of attentions from not only industrial but also academic circles. The objective of this article is to quantitatively review on deep learning and smart factory from 2010 to 2023. This research analyzed the 138 articles, extracted from the Core Collection of Web of Science, in terms of four dimensions such as the main trend in article publications, the main trend in article citations, the distribution of article publications by research area, and the keywords representing the main contents of published articles. The quantitative review results reveal the following four points: First, the article publications drastically grew from 2019 to 2022 in its annual trend. Second, the article citations have rapidly grown since 2018. Third, Engineering, Computer Science, and Telecommunications are the top 3 research areas composing the 138 articles. Fourth, it is the top 10 keywords such as 'deep', 'learning', 'smart', 'detection', factory', 'data', 'system', 'manufacturing', 'neural', and 'network' that represent the main contents of the 138 articles published from 2010 to 2023 in deep learning and smart factory. These findings revealed by this quantitative review will be significantly useful for deepening and widening relevant future research on deep learning and smart factory.

키워드

참고문헌

  1. P. P. Shinde and S. Shah, "A review of machine learning and deep learning applications," Fourth international conference on computing communication control and automation (ICCUBEA), IEEE, pp. 1-6, 2018. DOI: https//doi.org 10.1109/ICCUBEA.2018.8697857
  2. S. Makridakis, "The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms." Futures, Vol. 90, pp. 46-60. 2017. DOI: https://doi.org/10.1016/j.futures.2017.03.006
  3. P. Osterrieder, L. Budde, and T. Friedli, "The smart factory as a key construct of industry 4.0: A systematic literature review," International Journal of Production Economics, Vol. 221, 2020. DOI: https://doi.org/10.1016/j.ijpe.2019.08.011
  4. B. Chen, J. Wan, L. Shu, P. Li, M. Mukherjee, and B. Yin, "Smart factory of industry 4.0: Key technologies, application case, and challenges," IEEE Access, Vol. 6, pp. 6505-6519, 2017. DOI: https://doi.org/10.1109/access.2017.2783682
  5. N.E. Benti, M. D. Chaka, and A.G. Semie, "Forecasting Renewable Energy Generation with Machine learning and Deep Learning: Current Advances and Future Prospects," Sustainability, Vol. 15, 2023. DOI: https://doi.org/10.3390/su15097087
  6. S. Weng and H. Chen, "Exploring the role of deep learning technology in the sustainable development of the music production industry," Sustainability, Vol. 12, 2020. DOI: https://doi.org/10.3390/su12020625
  7. L. Li, K. Ota, and M. Dong, "Deep learning for smart industry: Efficient manufacture inspection system with fog computing," IEEE Transactions on Industrial Informatics, Vol. 14, pp. 4665-4673. 2018. DOI: https://doi.org/10.1109/tii.2018.2842821
  8. H. Hwang , "Design of Remote Management System for Smart Factory," International Journal of Internet, Broadcasting and Communication, Vol. 12, No. 4 pp. 109-121. 2020. DOI: https://doi.org/10.7236/IJIBC.2020.12.4.109
  9. L. Zhang, J. Ling, and M. Lin, "Artificial intelligence in renewable energy: A comprehensive bibliometric analysis." Energy Reports, Vol. 8, pp. 14072-14088. 2022. DOI: https://doi.org/10.1016/j.egyr.2022.10.347
  10. K. Zhang and Q. Liang, "Recent progress of cooperation on climate mitigation: A bibliometric analysis." Journal of Cleaner Production, Vol. 277, 2020. DOI: https://doi.org/10.1016/j.jclepro.2020.123495
  11. M. Aria and C. Cuccurullo, "bibliometrix: An R-tool for comprehensive science mapping analysis," Journal of Informetrics, Vol. 11, No. 4, pp. 959-975, 2017. DOI: https://doi.org/10.1016/j.joi.2017.08.007
  12. bibliometrix.org, BIBLIOMETRIX, https://www.bibliometrix.org/home.