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All issues Volume 129 (2021) SHS Web Conf., 129 (2021) 04003 References
Open Access
Issue
SHS Web Conf.
Volume 129, 2021
The 21st International Scientific Conference Globalization and its Socio-Economic Consequences 2021
Article Number 04003
Number of page(s) 9
Section Industry 4.0
DOI https://doi.org/10.1051/shsconf/202112904003
Published online 16 December 2021
  1. Kovacova, M., & Lăzăroiu, G. (2021). Sustainable organizational performance, cyber-physical production networks, and deep learning-assisted smart process planning in Industry 4.0-based manufacturing systems. Economics, Management, and Financial Markets, 16(3), 41–54. [CrossRef] [Google Scholar]
  2. Lăzăroiu, G., Machová, V., & Kucera, J. (2020). Connected and autonomous vehicle mobility: Socially disruptive technologies, networked transport systems, and big data algorithmic analytics. Contemporary Readings in Law and Social Justice, 12(2), 61–69. [CrossRef] [Google Scholar]
  3. Lăzăroiu, G., Kliestik, T., and Novak, A. (2021). Internet of Things smart devices, industrial artificial intelligence, and real-time sensor networks in sustainable cyber-physical production systems. Journal of Self-Governance and Management Economics, 9(1), 20–30. [Google Scholar]
  4. Lim, K. Y. H., Zheng, P., Chen, C.-H., & Huang, L. (2020). A digital twin-enhanced system for engineering product family design and optimization. Journal of Manufacturing Systems, 57, 82–93. [CrossRef] [Google Scholar]
  5. Liu, C., Jiang, P., & Jiang, W. (2020a). Web-based digital twin modeling and remote control of cyber-physical production systems. Robotics and Computer-Integrated Manufacturing, 64, 101956. [CrossRef] [Google Scholar]
  6. Liu, Q., Leng, J., Yan, D., Zhang, D., Wei, L., Yu, A., et al. (2020b). Digital twin-based designing of the configuration, motion, control, and optimization model of a flow-type smart manufacturing system. Journal of Manufacturing Systems. [Google Scholar]
  7. Lu, Y., Liu, C., Wang, K. I., Huang, H., & Xu, X. (2020). Digital twin-driven smart manufacturing: Connotation, reference model, applications and research issues. Robotics and Computer-Integrated Manufacturing, 61, 101837. [CrossRef] [Google Scholar]
  8. Lyons, N., and Lăzăroiu, G. (2020). Addressing the COVID-19 crisis by harnessing Internet of Things sensors and machine learning algorithms in data-driven smart sustainable cities. Geopolitics, History, and International Relations, 12(2), 65–71. [CrossRef] [Google Scholar]
  9. Ma, J., Chen, H., Zhang, Y., Guo, H., Ren, Y., Mo, R., et al. (2020). A digital twin-driven production management system for production workshop. The International Journal of Advanced Manufacturing Technology, 110, 1385–1397. [CrossRef] [Google Scholar]
  10. Mourtzis, D. (2020). Simulation in the design and operation of manufacturing systems: State of the art and new trends. International Journal of Production Research, 58(7), 1927–1949. [CrossRef] [Google Scholar]
  11. Nica, E., and Stehel, V. (2021). Internet of Things sensing networks, artificial intelligence-based decision-making algorithms, and real-time process monitoring in sustainable Industry 4.0. Journal of Self-Governance and Management Economics, 9(3), 35–47. [Google Scholar]
  12. Nica, E., Stan, C. I., Luțan (Petre), A. G., and Oașa (Geambazi), R.-Ș. (2021). Internet of Things-based real-time production logistics, sustainable industrial value creation, and artificial intelligence-driven big data analytics in cyber-physical smart manufacturing systems. Economics, Management, and Financial Markets, 16(1), 52–62. [CrossRef] [Google Scholar]
  13. Nguyen, H. X., Trestian, R., To, D., & Tatipamula, M. (2021). Digital twin for 5G and beyond. IEEE Communications Magazine, 59(2), 10–15. [CrossRef] [Google Scholar]
  14. Park, K. T., Lee, D., & Noh, S. D. (2020). Operation procedures of a work-center-level digital twin for sustainable and smart manufacturing. International Journal of Precision Engineering and Manufacturing-Green Technology, 7, 791–814. [CrossRef] [Google Scholar]
  15. Poliak, M., Baker, A., Konecny, V., & Nica, E. (2020). Regulatory and governance mechanisms for self-driving cars: Social equity benefits and machine learning-based ethical judgments. Contemporary Readings in Law and Social Justice, 12(1), 58–64. [CrossRef] [Google Scholar]
  16. Popescu, G. H., Valaskova, K., & Majerova, J. (2020). Real-time sensor networks, advanced robotics, and product decision-making information systems in data-driven sustainable smart manufacturing. Economics, Management, and Financial Markets, 15(4), 29–38. [CrossRef] [Google Scholar]
  17. Popescu, G. H., Petreanu, S., Alexandru, B., & Corpodean, H. (2021). Internet of Things-based Real-Time Production Logistics, Cyber-Physical Process Monitoring Systems, and Industrial Artificial Intelligence in Sustainable Smart Manufacturing. Journal of Self-Governance and Management Economics, 9(2), 52–62. [Google Scholar]
  18. Scott, R., Poliak, M., Vrbka, J., & Nica, E. (2020). COVID-19 response and recovery in smart sustainable city governance and management: Data-driven Internet of Things systems and machine learning-based analytics. Geopolitics, History, and International Relations, 12(2), 16–22. [CrossRef] [Google Scholar]
  19. Sun, J., Tian, Z., Fu, Y., Geng, J., & Liu, C. (2021). Digital twins in human understanding: A deep learning-based method to recognize personality traits. International Journal of Computer Integrated Manufacturing, 34(7/8), 860–873. [CrossRef] [Google Scholar]
  20. Throne, O., & Lăzăroiu, G. (2020). Internet of Things-enabled sustainability, industrial big data analytics, and deep learning-assisted smart process planning in cyber-physical manufacturing systems. Economics, Management, and Financial Markets, 15(4), 49–58. [CrossRef] [Google Scholar]
  21. Wang, K. J., Lee, T. L., & Hsu, Y. (2020). Revolution on digital twin technology – A patent research approach. The International Journal of Advanced Manufacturing Technology, 107, 4687–4704. [CrossRef] [Google Scholar]
  22. Wang, P., & Luo, M. (2021). A digital twin-based big data virtual and real fusion learning reference framework supported by industrial internet towards smart manufacturing. Journal of Manufacturing Systems, 58(A), 16–32. [CrossRef] [Google Scholar]
  23. Xia, K., Sacco, C., Kirkpatrick, M., Saidy, C., Nguyen, L., Kircaliali, A., et al. (2021). A digital twin to train deep reinforcement learning agent for smart manufacturing plants: Environment, interfaces and intelligence. Journal of Manufacturing Systems, 58(B), 210–230. [CrossRef] [Google Scholar]
  24. Zheng, P., & Sivabalan, A. S. (2020). A generic tri-model-based approach for product-level digital twin development in a smart manufacturing environment. Robotics and Computer-Integrated Manufacturing, 64, 101958. [CrossRef] [Google Scholar]
  25. Zhou, Y., Xing, T., Song, Y., Li, Y., Zhu, X., Li, G., et al. (2021). Digital-twin-driven geometric optimization of centrifugal impeller with free-form blades for five-axis flank milling. Journal of Manufacturing Systems, 58(B), 22–35. [CrossRef] [Google Scholar]

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