The proposed UF-LC-ESI-MS method was a powerful tool for the discovery of AChEIs from traditional Chinese medicine (TCM).

A videogrammetric system is proposed for monitoring the 3-D deformation time history of a metal sheet surface during welding, which could not be achieved by traditional displacement sensors. Two commercial-grade digital video cameras are used for image acquisition. The major algorithms of the videogrammetric process such as target recognition, stereo matching, 3-D reconstruction, and target tracking, are discussed in detail. An improved self-calibration method based on a planar pattern is proposed and verified. Precision evaluation experiments prove that the proposed system could achieve an accuracy of 0.05 mm. Three welding deformation surveying experiments using different metal sheets are conducted to validate the performance of the videogrammetric system and the obtained data are compared with results from linear variable differential transducers. The agreement between videogrammetric and conventional results confirms the availability and reliability of the proposed videogrammetric system for monitoring dynamic welding deformation of metal sheets.

Aiming at rapidly inspect Blade of Large Water Turbine, a new technology based on optical measurement is proposed. The procedure is divided into three steps: firstly, industrial close-range photogrammetry technology is used to acquire 3D points on the workpiece surface; secondly, using 3 pairs point method and ICP methods to achieve spatial matching between point cloud model and CAD model; finally, an error calculation method for triangulated CAD model is proposed. New vertex directed searching methods promote measuring points attaching to triangulated surfaces of CAD model. Experiments show that the program can meet the engineering requirements. Compared with the TRITOP software, the proposed method makes significantly improvement in measurement accuracy and inspection efficiency.

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Sharp features of 3D point clouds play an important role in many geometric computations and modeling application .In this paper, a novel modified Partition of Unity (PoU) Based Sharp feature extraction algorithm is proposed, which is directly operated on discrete point clouds. For every point in target point cloud, spherical neighborhood with radius 8 is acquired with the help of KD-Tree and weighted average position of points within the 8 -neighborhood is computed using modified PoU method. Distance which is the projection of the displace between original point and its Weighted average position along normal direction is defined as the criteria for a point belong to sharp feature or crease line. Experiments on both synthetic data and practical scanner point clouds indicate that our algorithm are both efficient and effective to the task of sharp feature extraction from point clouds .Our method is easy to be implemented and more sensitive to sharp features as well as its low computational complexity.