Science, Technology, Engineering and Mathematics.
Open Access

CONSTRUCTION AND PRACTICE OF "POST-COURSE-COMPETITION-CERTIFICATE" TALENT TRAINING MODE FOR INTERNET OF THINGS APPLICATION TECHNOLOGY MAJOR FOR INTELLIGENT CIVIL AVIATION

Download as PDF

Volume 2, Issue 3, Pp 59-68, 2024

DOI: https://doi.org/10.61784/wjes3062

Author(s)

YuTong Chen

Affiliation(s)

Guangzhou Civil Aviation College, Guangzhou 510403, Guangdong, China.

Corresponding Author

YuTong Chen

ABSTRACT

Against the backdrop of accelerated development in smart civil aviation, the aviation industry urgently requires interdisciplinary technical professionals who combine core IoT capabilities with aviation industry expertise. This study examines talent cultivation pathways for IoT professionals in smart aviation through the talent development framework of Guangzhou Civil Aviation Vocational College's IoT Application Technology program. The analysis systematically explores five dimensions: professional positioning, curriculum design, practical training, faculty development, and quality assurance. Research findings indicate that implementing a model anchored by industry demands, using job requirements to guide curriculum design, integrating virtual and real-world training systems, ensuring teaching quality through dual-teacher teams, and adopting a closed-loop evaluation system can effectively cultivate high-caliber professionals qualified for IoT equipment maintenance, logistics system operations, and intelligent system integration roles. These insights provide practical references for cultivating aviation-related technical talents in vocational education.

KEYWORDS

Smart civil aviation; Internet of Things application technology; Higher vocational education; Post-course competition certificate; Talent training mode

CITE THIS PAPER

YuTong Chen. Construction and practice of "post-course-competition-certificate" talent training mode for Internet of Things application technology major for intelligent civil aviation. World Journal of Educational Studies. 2024, 2(3): 59-68. DOI: https://doi.org/10.61784/wjes3062.

REFERENCES

[1] Al-Dhaqm A, Ikuesan RA, Kebande VR, et al. Research challenges and opportunities in drone forensics models. Electronics, 2021, 10: 1519.

[2] Chen Y, Liu Y. Design and implementation of routing nodes in airport intelligent bird control system. Computer Applications and Software, 2020, 10.

[3] Shi X, Yang C, Xie W, et al. Anti-drone system with multiple surveillance technologies: architecture, implementation, and challenges. IEEE Communications Magazine, 2018, 56: 68–74.

[4] Zhang C, Li G, Lin J, et al. Design and experiment of intelligent bird repellent for farmland. Journal of Southwest Normal University (Natural Edition), 2016, 41(5): 81-87.

[5] Nowak A, Naus K, Maksimiuk D. A method of fast and simultaneous calibration of many mobile FMCW radars operating in a network anti-drone system. Remote Sensing, 2019, 11: 2617.

[6] Oh BS, Guo X, Wan F, et al. Micro-Doppler mini-UAV classification using empirical-mode decomposition features. IEEE Geoscience and Remote Sensing Letters, 2017, 15: 227–231.

[7] Torvik B, Olsen KE, Griffiths H. Classification of birds and UAVs based on radar polarimetry. IEEE Geoscience and Remote Sensing Letters, 2016, 13: 1305–1309.

[8] Wang M, Wu Q. Multi UAV collaborative airport bird drive task allocation. Journal of Jilin University (Information Science Edition), 2019, 37(1): 47-57.

[9] Ren J, Jiang X. Regularized 2D complex-log spectral analysis and subspace reliability analysis of micro-Doppler signature for UAV detection. Pattern Recognition, 2017, 69: 225–237.

[10] Redmon J, Divvala S, Girshick R, et al. Only look once: unified, real-time object detection, 2016. Available: http://pjreddie.com/yolo/

[11] Taha B, Shoufan A. Machine learning-based drone detection and classification: state-of-the-art in research. IEEE Access, 2019, 7: 138669–138682.

[12] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. International Conference on Learning Representations (ICLR), 2015.

[13] Tuchin VV. Polarized light interaction with tissues. Journal of Biomedical Optics, 2016, 21: 071114.

[14] Anwar MZ, Kaleem Z, Jamalipour A. Machine learning inspired sound-based amateur drone detection for public safety applications. IEEE Transactions on Vehicular Technology, 2019, 68: 2526–2534.

[15] Azari MM, Sallouha H, Chiumento A, et al. Key technologies and system trade-offs for detection and localization of amateur drones. IEEE Communications Magazine, 2018, 56: 51–57.

[16] Liu L, Li X, Nonaka K. Light depolarization in off-specular reflection on submicro rough metal surfaces with imperfectly random roughness. Rev Sci Instrum, 2015, 86: 023107.

[17] Fioranelli F, Ritchie M, Griffiths H, et al. Classification of loaded/unloaded micro-drones using multistatic radar. Electron Lett, 2015, 51: 1813–1815.

[18] Luo D, Xue Y, Deng X, et al. Citrus diseases and pests detection model based on self-attention YOLOv8. IEEE Access, 2023, 11: 139872–139881.

[19] Liu X, Wang Y, Yu D, et al. YOLOv8-FDD: A real-time vehicle detection method based on improved YOLOv8. IEEE Access, 2024, 12: 136280–136296.

[20] Huang T, Huang H, Li Z, et al. Citrus fruit recognition method based on improved YOLOv5 model. J Huazhong Agric Univ, 2022, 41: 170–177.

[21] Ye R, Shao G, He Y, et al. YOLOv8-RMDA: Lightweight YOLOv8 network for early detection of small target diseases in tea. Sensors, 2024, 24(9): 1424–8220.

All published work is licensed under a Creative Commons Attribution 4.0 International License. sitemap
Copyright © 2017 - 2025 Science, Technology, Engineering and Mathematics.   All Rights Reserved.