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http://dx.doi.org/10.6109/jkiice.2022.26.9.1272

AIoT-based High-risk Industrial Safety Management System of Artificial Intelligence  

Yeo, Seong-koo (Department of Convergence Engineering, Hoseo Graduate School of Venture)
Park, Dea-Woo (Department of Convergence Engineering, Hoseo Graduate School of Venture)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.26, no.9, 2022 , pp. 1272-1278 More about this Journal
Abstract
The government enacted and promulgated the 'Severe Accident Punishment Act' in January 2021 and is implementing this law. However, the number of occupational accidents in 2021 increased by 10.7% compared to the same period of the previous year. Therefore, safety measures are urgently needed in the industrial field. In this study, BLE Mesh networking technology is applied for safety management of high-risk industrial sites with poor communication environment. The complex sensor AIoT device collects gas sensing values, voice and motion values in real time, analyzes the information values through artificial intelligence LSTM algorithm and CNN algorithm, and recognizes dangerous situations and transmits them to the server. The server monitors the transmitted risk information in real time so that immediate relief measures are taken. By applying the AIoT device and safety management system proposed in this study to high-risk industrial sites, it will minimize industrial accidents and contribute to the expansion of the social safety net.
Keywords
AIoT; Complex sensor device; BLE Mesh Network; Lightweight Deep Learning;
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Times Cited By KSCI : 1  (Citation Analysis)
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1 M. S. Kim, K. H. Kim, H. C. Choi, and H. Park, "A study on wireless mesh networks composition algorithm," in Proceedings of the Korean Society of Electronics Engineers Conference, Incheon, Korea, pp. 458-459, 2017.
2 N. Golmie, O. Rebala, and N. Chevrollier, "Bluetooth adaptive frequency hopping and scheduling," in Proceedings of IEEE Military Communications Conference, Boston: MA, USA, vol. 2, pp. 1138-1142, 2003.
3 S. -U. Park, "Analysis of the Status of Natural Language Processing Technology Based on Deep Learning," Journal of the Korean Big Data Society, vol. 6, no. 1, pp. 63-81, Aug. 2021.
4 A. Kamilaris and F. X. Prenafeta-Boldu, "Deep learning in agriculture: A survey," Computers and Electronics in Agriculture, vol. 147, pp. 70-90, Apr. 2018.   DOI
5 Y. T. Jun and E. H. Lim, "A method of motion recognition for elderly people using tri-axial accelerometer," in Proceedings of the Society of CAD/CAM Conference, Yongin, Korea, pp. 570-573, 2015.
6 F. Chollet, "Xception: Deep Learning with Depthwise Separable Convolutions," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu: HI, USA, pp. 1800-1807, 2017.
7 Ministry of Employment and Labor. Publication of the number of industrial accidents in 21 years, etc. [Internet]. Available:https://www.moel.go.kr/info/publict/publictDataView.do?bbs_seq=20211201749.
8 S. Max and T. Wang, "Transmit Power Control in Wireless Mesh Networks Considered Harmful," in Proceedings of 2009 Second International Conference on Advances in Mesh Networks, Athens, Greece, pp. 73-78, 2009.