Abstract:As an important component of urban vegetation, street trees play an important role in maintenance of environmental quality, aesthetic beauty of urban landscape, and social service for inhabitants. Acquiring accurate and up-to-date inventory information for street trees is required for urban horticultural planning, and municipal urban forest management. This paper presents a new Voxel-based Marked Neighborhood Searching (VMNS) method for efficiently identifying street trees and deriving their morphological parameters from Mobile Laser Scanning (MLS) point cloud data. The VMNS method consists of six technical components: voxelization, calculating values of voxels, searching and marking neighborhoods, extracting potential trees, deriving morphological parameters, and eliminating pole-like objects other than trees. The method is validated and evaluated through two case studies. The evaluation results show that the completeness and correctness of our method for street tree detection are over 98%. The derived morphological parameters, including tree height, crown diameter, diameter at breast height (DBH), and crown base height (CBH), are in a good agreement with the field OPEN ACCESS Remote Sens. 2013, 5 585 measurements. Our method provides an effective tool for extracting various morphological parameters for individual street trees from MLS point cloud data.

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Computer aided design and numerical simulation have been widely applied in optimization design of fan blades. In this paper, skew and sweep parameters of two-stage blades of an axial fan are optimized by using the particle swarm optimization algorithm. First, the skew and sweep parameters of two-stage blades of an axial fan are defined. Second, response surface methodology is used to study the relationship between the skew and sweep parameters of two-stage blades and the total pressure and the efficiency of the axial fan. The response surface model that describes the relationship between the skew and sweep of two-stage blades and the total pressure and the efficiency of the axial fan is established. Finally, with the skew and sweep of two-stage blades being design variables and the total pressure and the efficiency being the objectives, a particle swarm optimization algorithm is developed to solve this complex multiobjective optimization problem. The optimal result shows that the total pressure increases by 49.1 Pa and the efficiency increases by 1.55%. In addition, the aerodynamic performance of the axial fan is improved. This research has significance to optimization design of the axial fan.