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| 1 | A Case History of Shield Tunnel Crossing through Group Pile Foundation of a Road Bridge with Pile Underpinning Technologies in Shanghai 显示文摘 | XU Qianwei ZHU Hehua MA Xianfeng | 2015 | Tunnelling and Underground Space Technology2015，，45： | 1 |
| 2 | Point-cloud segmentation of individual trees in complex natural forest scenes based on a trunk-growth method显示文摘Forest resource management and ecological assessment have been recently supported by emerging technologies.Terrestrial laser scanning(TLS)is one that can be quickly and accurately used to obtain three-dimensional forest information,and create good representations of forest vertical structure.TLS data can be exploited for highly significant tasks,particularly the segmentation and information extraction for individual trees.However,the existing single-tree segmentation methods suffer from low segmentation accuracy and poor robustness,and hence do not lead to satisfactory results for natural forests in complex environments.In this paper,we propose a trunk-growth(TG)method for single-tree point-cloud segmentation,and apply this method to the natural forest scenes of Shangri-La City in Northwest Yunnan,China.First,the point normal vector and its Z-axis component are used as trunk-growth constraints.Then,the points surrounding the trunk are searched to account for regrowth.Finally,the nearest distributed branch and leaf points are used to complete the individual tree segmentation.The results show that the TG method can effectively segment individual trees with an average F-score of 0.96.The proposed method applies to many types of trees with various growth shapes,and can effectively identify shrubs and herbs in complex scenes of natural forests.The promising outcomes of the TG method demonstrate the key advantages of combining plant morphology theory and LiDAR technology for advancing and optimizing forestry systems. | Qianwei Liu Weifeng Ma Jianpeng Zhang Yicheng Liu Dongfan Xu Jinliang Wang | 2021 | Journal of Forestry Research2021，32，6： | 1 |
| 3 | Filtration characteristics of a binary multi-layer granular bed flter based on CFD-DEM coupling simulation显示文摘The coupled CFD-DEM method with the JKR(Johnson-Kendall-Roberts)model for describing the contact adhesion of dust to filter particles(FPs)is used to simulate the distribution pattern of dust particle deposition in the granular bed filter(GBF)with multi-layer media.The minimum inlet flow velocity must meet the requirement that the contact probability between dust and FPs is in the high contact probability region.The air flow forms vortices on the leeward side of the FPs and changes abruptly at the intersection of different particle size FPs layers.Dust particles form large deposits at the intersection of the first and second layers and the different particle size filter layers.Dual element multilayer GBF can further optimize the bed structure by interlacing filter layers with different particle sizes.Compared with single particle size multi-layer GBF,the bed pressure drop is reduced by 40.24%-50.65%and the dust removal efficiency is increased by 21.93%-55.09%. | Hongsheng Ding Qianwei Ma Lige Tong Shaowu Yin Li Wang Yulong Ding | 2023 | Particuology2023，，7： | 0 |
| 4 | Natural forest ALS-TLS point cloud data registration without control points显示文摘Airborne laser scanning(ALS)and terrestrial laser scanning(TLS)has attracted attention due to their forest parameter investigation and research applications.ALS is limited to obtaining fi ne structure information below the forest canopy due to the occlusion of trees in natural forests.In contrast,TLS is unable to gather fi ne structure information about the upper canopy.To address the problem of incomplete acquisition of natural forest point cloud data by ALS and TLS on a single platform,this study proposes data registration without control points.The ALS and TLS original data were cropped according to sample plot size,and the ALS point cloud data was converted into relative coordinates with the center of the cropped data as the origin.The same feature point pairs of the ALS and TLS point cloud data were then selected to register the point cloud data.The initial registered point cloud data was fi nely and optimally registered via the iterative closest point(ICP)algorithm.The results show that the proposed method achieved highprecision registration of ALS and TLS point cloud data from two natural forest plots of Pinus yunnanensis Franch.and Picea asperata Mast.which included diff erent species and environments.An average registration accuracy of 0.06 m and 0.09 m were obtained for P.yunnanensis and P.asperata,respectively. | Jianpeng Zhang Jinliang Wang Feng Cheng Weifeng Ma Qianwei Liu Guangjie Liu | 2023 | Journal of Forestry Research2023，34，3： | 0 |
| 5 | Effect of pressure cooking on phenolic compounds of quinoa显示文摘Due to good nutritional properties and potential health benefits,quinoa has gained an increasing attention.The study aimed to analyze the effect of pressure cooking on the composition,antioxidant activity,antibacterial activity and bioavailability of phenolic compounds in four types of quinoa,and to evaluate the correlation between phenolics and its biological activities by correlation analysis.The results showed that different varieties of quinoa contained different phenolic components and their biological activities were different.Pressure cooking could significantly increase(P<0.05)the phenolic contents of quinoa,and decrease in vitro digestibility of protein.The antioxidant activity,antibacterial activity and bioavailability of quinoa were also enhanced which were positively related with phenolic contents.HPLC analysis indicated that at least twelve phenolic compounds were found in quinoa,and hyperoside,sinapic acid,rutin and ferulic acid occupied a majority of them.Correlation analysis suggested that hyperoside,quercetin,sinapic acid,ferulic acid and gallic acid made the key contribution to antioxidant and antibacterial activities of phenolic compounds of quinoa.The results provided valuable information for quinoa processing with phenolics as functional ingredient. | Qianwei Ma Zhixiang Li Thanutchaporn Kumrungsee Wenda Huang Ruge Cao | 2023 | Grain & Oil Science and Technology2023，6，3： | 0 |
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