Description: Remote Sensing, Vol. 10, Pages 1078: Airborne LiDAR Remote Sensing for Individual Tree Forest Inventory Using Trunk Detection-Aided Mean Shift Clustering Techniques Remote Sensing doi: 10.3390/rs10071078 Authors: Wei Chen Xingbo Hu Wen Chen Yifeng Hong Minhua Yang Airborne LiDAR (Light Detection And Ranging) remote sensing for individual tree-level forest inventory necessitates proper extraction of individual trees and accurate measurement of tree structural parameters. Due to the inadequate tree finding capability offered by LiDAR technology and the complex patterns of forest canopies, significant omission and commission errors occur frequently in the segmentation results. Aimed at error reduction and accuracy refinement, this paper presents a novel adaptive mean shift-based clustering scheme aided by a tree trunk detection technique to segment individual trees and estimate tree structural parameters based solely on the airborne LiDAR data. Tree trunks are detected by analyzing points’ vertical histogram to detach all potential crown points and then clustering the separated trunk points according to their horizontal mutual distances. The detected trunk information is used to adaptively calibrate the kernel bandwidth of the mean shift procedure in the fine segmentation stage by applying an original 2D (two-dimensional) estimation of individual crown diameters. Trunk detection results and LiDAR point clusters generated by the adaptive mean shift procedures serve as mutual references for final detection of individual trees. Experimental results show that a combination of adaptive mean shift clustering and detected tree trunk can provide a significant performance improvement in individual tree-level forest measurement. Compared with conventional clustering techniques, the trunk detection-aided mean shift clustering approach can detect 91.1% of the trees (“recall”) with a higher tree positioning accuracy (the mean positioning error is reduced by 33%) in a multi-layered coniferous and broad-leaved mixed forest in South China, and 93.5% of the identified trees are correct (“precision”). The tree detection brings the estimation of structural parameters for individual trees up to an accuracy level: −2.2% mean relative error and 5.8% relative RMSE (Root Mean Square Error) for tree height and 0.6% mean relative error and 21.9% relative RMSE for crown diameter, respectively.

Description: Energies, Vol. 11, Pages 2240: A Variable-Structure Multi-Resonant DC–DC Converter with Smooth Switching Energies doi: 10.3390/en11092240 Authors: Mengying Chen Yifeng Wang Liang Yang Fuqiang Han Yuqi Hou Haiyun Yan In this paper, a variable-structure multi-resonant soft-switching DC–DC converter and its transient smooth control method are proposed. Through the introduction of auxiliary switches, the converter can flexibly adjust its structure among three operating modes. Two switching processes can be obtained. Thus, a wide voltage gain range is achieved within a narrow frequency range. Moreover, to eliminate the large voltage fluctuation during modes switching, a drive signal gradual adjustment control method is proposed. Consequently, smooth switching between different modes can be realized and the voltage fluctuation is suppressed effectively. Finally, a 200 W experimental prototype is established to verify the theoretical analyses. Soft-switching performances for power switches and diodes are both guaranteed. The highest efficiency is 98.2%. With the proposed transient control method, a basically constant 400 V output voltage is ensured within a wide input voltage range (80 V–600 V). In particular, the transient voltage fluctuations during two switching processes decrease from 38.4 V to 10.8 V and from 37.2 V to 8.4 V, respectively.