Node deployment optimization is one of the application points of wireless sensor networks (WSN), and it is also a hot topic that scholars have studied in recent years. Aiming at the problems that exist in WSN node dep...
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ISBN:
(纸本)9781538675878
Node deployment optimization is one of the application points of wireless sensor networks (WSN), and it is also a hot topic that scholars have studied in recent years. Aiming at the problems that exist in WSN node deployment, such as uneven distribution of nodes and incomplete coverage, this paper proposes an optimization method for WSN node deployment based on ant-lion optimization algorithm. Firstly, this paper establishes a mathematical model to cover the target area with WSN nodes. Then, the optimization problem of node deployment is transformed into the problem of finding the maximum value of the function. Finally, the ALO algorithm is used to obtain the optimal solution of node deployment. The experimental results show this method works well in optimizing the deployment location of WSN nodes, and can increase the overall network coverage rate.
With the escalating demand for underground mining and infrastructure construction, the optimization of tunnel construction has emerged as a primary concern for researchers. The geological conditions encountered during...
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With the escalating demand for underground mining and infrastructure construction, the optimization of tunnel construction has emerged as a primary concern for researchers. The geological conditions encountered during the excavation of hard rock tunnels using tunnel boring machines (TBM) significantly impact construction efficiency and cost-effectiveness. The existing lithology testing methods need to be more efficient in aligning with TBM operational efficiency. In recent years, the rapid advancement of artificial intelligence has paved the way for its integration into numerous domains, including tunnel engineering. To address this issue, this study proposes three innovative hybrid RF-based intelligent models, namely PSO-RF, ALO-RF, and GWO-RF, for the precise prediction of lithology in hard rock tunnels using TBM working parameters. The TBM operating parameters of the Jilin Yinsong Water Supply Project serve as the basis for this investigation. Twelve distinct characteristic parameters relevant to the lithology of the tunnel working face were carefully selected as input parameters for lithology prediction. Comparative analysis of the three hybrid models reveals that GWO-RF demonstrates exceptional lithology prediction performance (ACC = 0.999924;PREA = 0.0.9999976;RECA = 0.999775;F1A = 0.999876;Kappa = 0.999911), whereas PSO-RF and ALO-RF exhibit slightly inferior performance. Nonetheless, all three hybrid models exhibit a significant improvement in prediction accuracy compared to the unoptimized RF model. The research findings presented herein facilitate the swift determination of TBM working surface lithology, enabling timely adjustment of TBM working parameters, reducing equipment wear and tear, and enhancing construction efficiency.
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