Mr. Shurui Fan Profile

Shurui Fan

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Proceedings Article | 10 July 2024 Paper

Spatiotemporal continuous PM2.5 concentrations inversion based on multisource data and hybrid model

Li Wang, Lili Xu, Zhiyong Li, Ziyu Ren, Shurui Fan

Proceedings Volume 13223, 132232F (2024) https://doi.org/10.1117/12.3035494

KEYWORDS: Data modeling, Satellites, Atmospheric modeling, Machine learning, Meteorology, Reflectivity, Performance modeling

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Air pollution affects public health, destroys ecological environment and even aggravates climate change, and PM_2.5 is an important factor causing pollution. Therefore, accurate and continuous spatiotemporal PM_2.5 concentration inversion is of great significance for air pollution control. Nevertheless, current high temporal resolution inversions are predominantly conducted using Top-of-atmosphere reflectance, nighttime concentration inversion cannot be realized in the visible band. Moreover, existing hybrid models often rely on linear integrated, increasing the risk of overfitting. Therefore, we propose an improved stacking model that contains XGBoost and CatBoost model, stacked by elastic net regression. By fusing features from multi-source data and accounting for spatiotemporal heterogeneity, the model enables the simultaneous inversion of daytime and nighttime PM_2.5 concentrations. Compared to using Top-of-atmosphere reflectance alone, the daytime inversion results demonstrate a 1.08% increase in R² and a reduction of 1.34μg/m³ in RMSE. Meanwhile, the nighttime inversion results show a 2.04% improvement in R² and reduce the MAE to 9.54μg/m³ when spatiotemporal features are used. Applying the model to the Beijing-Tianjin-Hebei region enables spatiotemporal continuous inversion of PM_2.5 concentrations in the region.

Proceedings Article | 6 May 2024 Paper

Research on informed-RRT* algorithm for multi-strategy optimization in mobile robot path planning

Xiaoli Feng, Kewen Xia, Shurui Fan, Li Wang, Xin Zhao

Proceedings Volume 13107, 131073Z (2024) https://doi.org/10.1117/12.3029208

KEYWORDS: Mobile robots, Interpolation, Computer simulations, Design, Particle swarm optimization

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Path planning is a key technology to realize the autonomous navigation of mobile robots. The Informed-RRT* algorithm is the current path planning algorithm that solves the high sampling efficiency in complex environments, but it also suffers from long planning times and redundant turns in complex environments. For this reason, a multi-strategy optimization Informed-RRT* algorithm is proposed, the first is to introduce the WOA algorithm during the operation of re-selecting the parent node so that the new node can find the selection of the optimal parent node in the search radius, to improve the efficiency of the search, and the second is to select a suitable Bessel curve to interpolate and optimize the generated paths, to make the generated feasible paths smoother and with fewer redundant turns. In the algorithm validation, the McNamee wheeled robot is modeled using the joint simulation platform of ROS and GAZEBO, and the improved Informed RRT* algorithm is encapsulated into the ROS-based path planning algorithm, and the experimental results show that the proposed algorithm outperforms the existing algorithms in terms of the average planning time, the planning length, and the success rate of the planning, and it provides a new feasible solution for the path planning in the complex environment.

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