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Applications of Artificial Intelligence Techniques in Industry 4.0

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Abstract

The steady-state industry status has been changed to dynamic industry by the industrial revolution, so manufacturers have been pushed by the global market to reconsider their conventional manufacturing methods. Modern manufacturing needs new manufacturing operations, and effective factory management has a great value in this area. Recent advances in technology and modern industrial engineering systems from production to transportation have created a great need to track and identify the materials, products, and even live subjects [1].

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References

  1. S.S. Kamble, A. Gunasekaran, S.A. Gawankar, Sustainable Industry 4.0 framework: a systematic literature review identifying the current trends and future perspectives. Process Saf. Environ. Prot. 117, 408–425 (2018)

    Article  Google Scholar 

  2. R.R. Thapa et al., in Application of RFID Technology to Reduce Overcrowding in Hospital Emergency Departments, ed. by A.G. Nilsson, R. Gustas, G. Wojtkowski, W. Wojtkowski, S. Wrycza, J. Zupancic, Advances in Information Systems Development (Springer, 2018), pp. 17–32

    Google Scholar 

  3. M. Ma, P. Wang, C.-H. Chu, Redundant reader elimination in large-scale distributed RFID networks. IEEE Internet Things J (2018)

    Google Scholar 

  4. M.S. Altaf et al., Integrated production planning and control system for a panelized home prefabrication facility using simulation and rfid. Autom Constr 85, 369–383 (2018)

    Article  Google Scholar 

  5. S. Luthra et al., in Internet of Things (IoT) in Agriculture Supply Chain Management: A Developing Country Perspective, ed. by Y.K. Dwivedi, N.P. Rana, E.L. Slade, M.A. Shareef, A.C. Simintiras, B. Lal, Emerging Markets from a Multidisciplinary Perspective (Springer, 2018), pp. 209–220

    Google Scholar 

  6. W. Shi et al., Optimizing Directional Reader Antennas Deployment in UHF RFID localization system by using a MPCSO algorithm. IEEE Sens. J. 18(12), 5035–5048 (2018)

    Article  Google Scholar 

  7. A. Raghib, B.A. El Majd, B. Aghezzaf, in An Optimal Deployment of Readers for RFID Network Planning Using NSGA-II, ed. by A. Lionel, E.L. Talbi, F. Yalaoui, Recent Developments in Metaheuristics (Springer, 2018), pp. 463–476

    Google Scholar 

  8. M. Munkailu, S. Sani, A. Tekanyi, Development of a sparse RFID reader deployment algorithm for effective RFID network planning. Niger. J. Technol. 37(3), 779–785 (2018)

    Article  Google Scholar 

  9. P. Vestenický, M. Vestenický, in 2018 ELEKTRO, ed. by P. Vestenický, M. Vestenický, Mathematical Model of Non-linear RFID Reader—Transponder System (IEEE, 2018)

    Google Scholar 

  10. F. Amato, H.M. Torun, G. Durgin, RFID backscattering in long-range scenarios. IEEE Trans. Wireless Commun. (2018)

    Google Scholar 

  11. B.W. Podaima et al., Modeling RFID tracking in healthcare. CMBES Proc. 33(1) (2018)

    Google Scholar 

  12. A. Suriya, J.D. Porter, in Proceedings of IIE Annual Conference, ed. by A. Suriya, J.D. Porter, RFID Network Modeling and Optimization for Inventory Management (Institute of Industrial and Systems Engineers (IISE), 2012)

    Google Scholar 

  13. R.V. Aroca et al., Calibration of passive UHF RFID tags using neural networks to measure soil moisture. J. Sens. (2018)

    Google Scholar 

  14. A. Azizi, A. Vatankhah Barenji, M. Hashmipour, Optimizing radio frequency identification network planning through ring probabilistic logic neurons. Adv. Mech. Eng. 8(8), 1687814016663476 (2016)

    Article  Google Scholar 

  15. C.O. Chan, H. Lau, Y. Fan, in 2018 International Conference on Artificial Intelligence and Big Data (ICAIBD), ed. by C.O. Chan, H. Lau, Y. Fan, IoT Data Acquisition in Fashion Retail Application: Fuzzy Logic Approach (IEEE, 2018)

    Google Scholar 

  16. W. Zhu, M. Li, RFID reader planning for the surveillance of predictable mobile objects. Procedia Comput. Sci. 129, 475–481 (2018)

    Article  Google Scholar 

  17. X. Cai, L. Ye, Q. Zhang, Ensemble learning particle swarm optimization for real-time UWB indoor localization. EURASIP J. Wireless Commun. Netw. 2018(1), 125 (2018)

    Article  Google Scholar 

  18. S.K. Goudos, in Encyclopedia of Information Science and Technology, 4th edn, ed. by S.K. Goudos, Optimization of Antenna Arrays and Microwave Filters Using Differential Evolution Algorithms (IGI Global, 2018), pp. 6595–6608

    Google Scholar 

  19. L. Ma et al., Two-level master-slave RFID networks planning via hybrid multiobjective artificial bee colony optimizer. IEEE Trans. Syst. Man Cybern. Syst. (2017)

    Google Scholar 

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Authors and Affiliations

  1. Department of Engineering, German University of Technology in Oman, Muscat, Oman

    Aydin Azizi

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  1. Aydin Azizi
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Correspondence to Aydin Azizi .

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Azizi, A. (2019). Introduction. In: Applications of Artificial Intelligence Techniques in Industry 4.0. SpringerBriefs in Applied Sciences and Technology. Springer, Singapore. https://doi.org/10.1007/978-981-13-2640-0_1

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  • DOI: https://doi.org/10.1007/978-981-13-2640-0_1

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-2639-4

  • Online ISBN: 978-981-13-2640-0

  • eBook Packages: EngineeringEngineering (R0)

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