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Research on fire smoke traceability based on emotional intelligence Jaya algorithm

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

Bulletin of the Polish Academy of Sciences. Technical Sciences

2025

Vol. 73, nr 2

art. no. e152708

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.

Research on multi-UAV air pollution source localization algorithm based on emotional quantity

Lin Luyao, Ding Tao, Hu Lifang, Li Jinye

Environment Protection Engineering

2024

Vol. 50, nr 3

109--130

With the advancement of industrialization, the problem of atmospheric environmental pollution is becoming more and more prominent. To solve this problem, an unmanned aerial vehicle (UAV) as an airborne platform was used to design an air pollution source localization method based on an anxiety-auction algorithm and verify the feasibility of the algorithm through simulation analysis and indoor source localization experiments. The algorithm innovatively introduces the concept of anxiety in psyhology into the traditional auction algorithm. By enabling each drone to make a “rational” auction time decision based on its emotional state, team resources can be conserved, and overall source localization efficiency can be enhanced. Based on different environmental factors and conditions, the number of drones and other multi-perspective comparison analyses with the traditional auction algorithm, the analysis results show that the anxiety-auction algorithm performs better in terms of success rate and distance ratio. This paper also built a set of atmospheric pollutant source localization platforms, consisting of an ultra-wideband (UWB) indoor positioning device, UAV platform, source localization monitoring and control module, and the indoor source localization experiment of atmospheric pollutants based on multiple UAVs was successfully designed and carried out.