Analysis of the global development of technologies related to MAVs in the next decade

Document Type : Original Article

Author

Assistant professor of Aerospace Engineering, Imam Hasan Mojtabi University of Officers and Police Training, Tehran, Iran.

Abstract

In this article, an attempt has been made to analyze the global development of technologies related to MAVs in the next decade by using the basics of technology forecasting (monitoring and experts' views) and by using the fuzzy Delphi method. The current research is applied in terms of purpose, in terms of combined strategy, and in terms of implementation in the form of descriptive-survey research. The statistical population is in two levels. The first level is made up of experts in the field of the MAVs industry, and the second level is made up of university professors in the field of technologies related to MAVs. According to the purpose of the research, the targeted sampling number was 9 people in the first level and 35 people elected in the second level. The data collection tool is a researcher-made questionnaire, whose validity was determined by content method and reliability by Cronbach's alpha method. Data analysis was also done through the fuzzy Delphi method using Spss and Excel software.
The findings of this research show that the technologies related to MAVs can be counted in 29 indicators and out of these 9 indicators are in the field of artificial intelligence and electronics and telecommunication technologies are in priority and should be taken into consideration by the country's officials, decision-makers, and planners.

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Main Subjects

References

  • Altuntas, S. & Aba, S. (2022). Technology Forecasting of Unmanned Aerial Vehicle Technologies through Hierarchical S-Curves, Defence Science Journal, 72(1): 18-29, DOI: 10. 14429/dsj. 72. 16823
  • Antcliff, D. Keeley, P. Campbell, M. Oldham, J. & Woby, S. (2013). The development of an activity-pacing questionnaire for chronic pain and/or fatigue: a Delphi technique, Physiotherapy.
  • Battsengel, G. Geetha, S. & Jeon J. (2020). Analysis of technological trends and technological portfolio of unmanned aerial vehicle. Open Innov. Technol. Mark. Complex, 6(3): 48. doi: 10. 3390/joitmc6030048.
  • Bojadziev, G. & Bojadziev, M. (2007), Fuzzy Logic for Business, Finance, and Management, Advances in British Columbia Institute of Technology, Canada, 2nd Edition.
  • Ching-Hsue, C. & Yin, L. (2002). Evaluating the best main battle tank using fuzzy decision theory with linguistic criteria evaluation. European Journal of Operational Research, Vol. 142, p. 147.
  • Dalkey, N. & Helmer O. (1963). An experimental application of the Delphi method to the use of experts. Management Science, 9(3): 458–467.
  • Fahey, K. M. & Miller, M. J. (2017). Unmanned systems integrated roadmap FY 2017-2042, https: //www. defensedaily. com/wp-content/uploads/post_attachment/206477. pdf
  • GA, M. & Aewk, H. (2021). Enhanced fuzzy Delphi method in forecasting and decision-making. Advanced in Fuzzy System; P: 1–6. https: //doi. org/10. 1155/2021/2459573.
  • GlobalTrends2040, (2021). A Publication of the National Intelligent Council.
  • Habibi, A. Jahantigh, F. & Sarafrazi, A. (2015). Fuzzy Delphi Technique for Forecasting and Screening Items. Asian Journal of Research in Business Economics and Management, 5(2): 130-143.
  • Heiko, G. (2012). Consensus measurement in Delphi studies Re-opinion and implications for future quality assurance. Technological Forecasting & Social Change, Vol. 79, PP. 1525–1536
  • Liu, Q. Ge, Z. & Song, W. (2016). Research based on patent analysis about the present status and development trends of unmanned aerial vehicle in China. Open J. Soc. Sci. 4(7): 172–181. doi: 10. 4236/jss. 2016. 47027.
  • Mohamed, N. Al-Jaroodi, J. Jawhar I. Idries, A. & Mohammed, F. (2020). Unmanned aerial vehicles applications in future smart cities, Technological Forecasting and Social Change,Volume 153, https: //doi. org/10. 1016/j. techfore. 2018. 05. 004.
  • Mueller, T. J. (2009). On the Birth of Micro Air Vehicles. International Journal of Micro Air Vehicles, 1(1): 1-12.
  • Musa, M. S. Pauzi, S. F. Mahmud I. Zainal, M. A. Rahman, S. & Yaacob, Z. (2022). Revalidation of Women's Fishermen's Participation Scale in Entrepreneurship: A Fuzzy Delphi Method (FDM). International Journal of Academic Research in Business and Social Sciences, 12(9): 644–652.
  • Okoli, C. & Pawlowski S. (2004). The Delphi method as a research tool: An example, design considerations, and applications. Information & Management, 42(1): 15–29.
  • Powell, C. (2003). The Delphi technique: Myths and realities. Journal of Advanced Nursing, 41(4): 376-382.
  • Porter, A. L. & Rossini F. A. (1978). Technological Forecasting. In Encyclopedia of Systems and Control, ed. M. Singh. Oxford, Pergammon: 4823-4828.
  • PS, R. & Jeyan, M. L. (2020). Mini Unmanned Aerial Systems (UAV) - A Review of the Parameters for Classification of a Mini UAV. International Journal of Aviation, Aeronautics, and Aerospace, 7(3): 1-21.
  • Somerville, J. A. (2008). Effective Use of the Delphi Process in Research: Its Characteristics, Strengths, and Limitations. Unpublished doctoral dissertation, Oregon State University, Corvallis
  • Yusoff, M. Fkrudin, A. Azmil, H. , Norhisham, M. , Hamat, W. & Norina, W. (2021). Application of Fuzzy Delphi Technique to Identify the Elements for Designing and Developing the e-PBM PI-Poli Module. Asian Journal of University Education (AJUE). 17(1): p292-304.

 

 

 

 

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