Research on fire smoke traceability based on emotional intelligence Jaya algorithm

• Autorzy: Liu Xiaoxiao , Zhong Wenhan , Ding Tao , Lin Luyao , Mu Jinxia , Shi Jianghuan , Li Zesong

Identyfikatory

DOI

10.24425/bpasts.2024.152708

• Języki publikacji: EN

Abstrakty

EN

To timely detect fire smoke in the early stages and trace the gas generated, thereby avoiding the loss of human life and property and reducing damage to the ecological environment, this paper proposes a fire smoke tracing method based on the emotional intelligence Jaya algorithm (EIJaya). The algorithm assigns an anthropomorphic mental state to the unmanned aerial vehicle (UAV) in the traceability task to realize its self-evaluation and social evaluation. In the simulation concentration field, the EIJaya algorithm, the basic Jaya algorithm, and the PSO algorithm were used for the verification of the simulation of gas traceability, and the simulation results proved the advantages of the EIJaya algorithm in terms of the success rate and the iteration times. In this paper, the TT UAV was chosen as an experimental tool to utilize the functions of its expansion module fully, and the experimental hardware system was constructed by combining it with the corresponding sensors. The corresponding experimental scene was built in the indoor environment, and the EIJaya algorithm was used to make multiple UAVs cooperate and conduct traceability experiments, which verified the algorithm feasibility in practical applications and proved that the algorithm could quickly and accurately trace the fire smoke.

Słowa kluczowe

EN

fire smoke; gas traceability; EIJaya algorithm; UAV; unmanned aerial vehicle; Jaya algorithm; emotional intelligence Jaya algorithm; emotional intelligence

PL

śledzenie gazu; algorytm EIJaya; UAV; bezzałogowy statek powietrzny; inteligencja emocjonalna; algorytm Jaya; dym pożarowy

• Wydawca: Polska Akademia Nauk, Wydział IV Nauk Technicznych
• Czasopismo: Bulletin of the Polish Academy of Sciences. Technical Sciences
• Rocznik: 2025
• Tom: Vol. 73, nr 2
• Strony: art. no. e152708
• Opis fizyczny: Bibliogr. 17 poz., rys., wykr.

Twórcy

autor

Liu Xiaoxiao

• China Jiliang University, China

autor

Zhong Wenhan

• Zhejiang Light Industrial Products Inspection and Research Institute, China

autor

Ding Tao

• China Jiliang University, China

• https://orcid.org/0000-0003-4323-7625

autor

Lin Luyao

• China Jiliang University, China

autor

Mu Jinxia

• China Jiliang University, China

autor

Shi Jianghuan

• Ningbo Institute of Measurement and Testing, China

autor

Li Zesong

• Ningbo Institute of Measurement and Testing, China

Bibliografia

• [1] S. Loganathan and J. Arumugam, “Clustering algorithms for wireless sensor networks survey,” Sensor Lett., vol. 18, no. 2, pp. 143–149, 2020, doi: 10.1166/sl.2020.4193.
• [2] K. Langer, “A guide to sensor design for land mine detection,” in EUREL International Conference The Detection of Abandoned Land Mines: A Humanitarian Imperative Seeking a Technical Solution (Conf. Publ. No. 431), IET, 1996, pp. 30–32, doi: 10.1049/cp:19961073.
• [3] A.J. Bell, “Like moths to a flame: A review of what we know about pyrophilic insects,” Forest Ecol Manage., vol. 528, p. 120629, 2023, doi: 10.1016/j.foreco.2022.120629.
• [4] R. Rozas, J. Morales, and D. Vega, “Artificial smell detection for robotic navigation,” in Fifth International Conference on Advanced Robotics’ Robots in Unstructured Environments, IEEE, 1991, pp. 1730–1733, doi: 10.1109/icar.1991.240354.
• [5] V. Genovese, P. Dario, R. Magni, and L. Odetti, “Self organizing behavior and swarm intelligence in a pack of mobile miniature robots in search of pollutants,” in Proceedings of the IEEE/RSJ international conference on intelligent robots and systems, IEEE, 1992, vol. 3, pp. 1575–1582, doi: 10.1109/iros.1992.594225.
• [6] Z. Fu, Y. Chen, Y. Ding, and D. He, “Pollution source localization based on multi-UAV cooperative communication,” IEEE Access, vol. 7, pp. 29304–29312, 2019, doi: 10.1109/access.2019.2900475.
• [7] L. Heimsch et al.,“Carbon dioxide fluxes and carbon balance of an agricultural grassland in southern Finland,” Biogeosciences, vol. 18, no. 11, pp. 3467–3483, 2021, doi: 10.5194/bg-18-3467-2021.
• [8] L. Carlier, I. Rotar, M. Vlahova, and R. Vidican, “Importance and functions of grasslands,” Notulae Botanicae Horti Agrobotanici Cluj-Napoca, vol. 37, no. 1, pp. 25–30, 2009, doi: 10.15835/nbha3713090.
• [9] H. Liu, L. Hou, N. Kang, Z. Nan, and J. Huang, “The economic value of grassland ecosystem services: A global meta-analysis,” Grassland Res., vol. 1, no. 1, pp. 63–74, 2022, doi: 10.1002/glr2.12012.
• [10] O.F. Aje and A.A. Josephat, “The particle swarm optimization (PSO) algorithm application – A review,” Glob. J. Eng. Technol. Adv., vol. 3, no. 3, pp. 001–006, 2020, doi: 10.30574/gjeta.2020.3.3.0033.
• [11] H. Sheng et al., “An advanced gas leakage traceability & dispersion prediction methodology using unmanned aerial vehicle,” J. Loss Prev. Process Ind., vol. 88, p. 105276, 2024, doi: 10.1016/j.jlp.2024.105276.
• [12] Y. Liu, Y. Jiang, X. Zhang, Y. Pan, and Y. Qi, “Combined grey wolf optimizer algorithm and corrected Gaussian diffusion model in source term estimation,” Processes, vol. 10, no. 7, p. 1238, 2022, doi: 10.3390/pr10071238.
• [13] F. van Breugel, “Correlated decision making across multiple phases of olfactory-guided search in Drosophila improves search efficiency,” J. Exp. Biol., vol. 224, no. 16, p. 242267, 2021, doi: 10.1242/jeb.242267.
• [14] R. Rao, “Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems,” Int. J. Ind. Eng. Comput., vol. 7, no. 1, pp. 19–34, 2016, doi: 10.5267/j.ijiec.2015.8.004.
• [15] K. Yu, J. Liang, B. Qu, X. Chen, and H. Wang, “Parameters identification of photovoltaic models using an improved JAYA optimization algorithm,” Energy Conv. Manage., vol. 150, pp. 742–753, 2017, doi: 10.1016/j.enconman.2017.08.063.
• [16] T.A. Judge, E.A. Locke, C.C. Durham, and A.N. Kluger, “Dispositional effects on job and life satisfaction: the role of core evaluations,” J. Appl. Psychol., vol. 83, no. 1, p. 17, 1998, doi: 10.1037/0021-9010.83.1.17.
• [17] Y. Zhang and P. Zhang, “Machine training and parameter settings with social emotional optimization algorithm for support vector machine,” Pattern Recognit. Lett., vol. 54, pp. 36–42, 2015, doi: 10.1016/j.patrec.2014.11.011.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).