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| 1 | Evaluating the accuracy performance of Lucas-Kanade algorithm in the circumstance of PIV application显示文摘Lucas-Kanade(LK) algorithm, usually used in optical flow filed, has recently received increasing attention from PIV community due to its advanced calculation efficiency by GPU acceleration. Although applications of this algorithm are continuously emerging,a systematic performance evaluation is still lacking. This forms the primary aim of the present work. Three warping schemes in the family of LK algorithm: forward/inverse/symmetric warping, are evaluated in a prototype flow of a hierarchy of multiple two-dimensional vortices. Second-order Newton descent is also considered here. The accuracy & efficiency of all these LK variants are investigated under a large domain of various influential parameters. It is found that the constant displacement constraint, which is a necessary building block for GPU acceleration, is the most critical issue in affecting LK algorithm's accuracy, which can be somehow ameliorated by using second-order Newton descent. Moreover, symmetric warping outbids the other two warping schemes in accuracy level, robustness to noise, convergence speed and tolerance to displacement gradient, and might be the first choice when applying LK algorithm to PIV measurement. | PAN Chong XUE Dong XU Yang WANG JinJun WEI RunJie | 2015 | Science China(Physics,Mechanics & Astronomy)2015，58，10： | 9 |
| 2 | Dual-basis reconstruction techniques for tomographic PIV显示文摘As an inverse problem, particle reconstruction in tomographic particle image velocimetry attempts to solve a large-scale underdetermined linear system using an optimization technique. The most popular approach, the multiplicative algebraic reconstruction technique(MART), uses entropy as an objective function in the optimization. All available MART-based methods are focused on improving the efficiency and accuracy of particle reconstruction. However, those methods do not perform very well on dealing with ghost particles in highly seeded measurements. In this report, a new technique called dual-basis pursuit(DBP), which is based on the basis pursuit technique, is proposed for tomographic particle reconstruction. A template basis is introduced as a priori knowledge of a particle intensity distribution combined with a correcting basis to enable a full span of the solution space of the underdetermined linear system. A numerical assessment test with 2D synthetic images indicated that the DBP technique is superior to MART method, can completely recover a particle field when the number of particles per pixel(ppp) is less than 0.15, and can maintain a quality factor Q of above 0.8 for ppp up to 0.30. Unfortunately, the DBP method is difficult to utilize in 3D applications due to the cost of its excessive memory usage. Therefore, a dual-basis MART was designed that performed better than the traditional MART and can potentially be utilized for 3D applications. | YE ZhiJian GAO Qi WANG HongPing WEI RunJie WANG JinJun | 2015 | Science China(Technological Sciences)2015，58，11： | 4 |
| 3 | CFD and digital particle tracking to assess flow characteristics in the labyrinth flow path of a drip irrigation emitter显示文摘 | Yunkai Li Peiling Yang Tingwu Xu Shumei Ren Xiongcai Lin Runjie Wei Hongbing Xu | 2008 | Irrigation Science2008，，5： | 2 |
| 4 | CFD and digital particle tracking to assess flow characteristics in the labyrinth flow path of a drip irrigation emitter显示文摘 | Yunkai Li Peiling Yang Tingwu Xu Shumei Ren Xiongcai Lin Runjie Wei Hongbing Xu | 2008 | Irrigation Science2008，，5： | 1 |
| 5 | Digital holography particle image velocimetry for the measurement of 3Dt-3c flows显示文摘 | Shen Gongxin Wei Runjie | 2005 | Opt & Lasers in Eng2005，43，10： | 1 |
| 6 | Digital holography particle image velocimetry for the measurement of 3Dt-3c flows显示文摘 | Shen Gongxin Wei Runjie | 2005 | Optics and Lasers in Engineering2005，43，10： | 1 |
| 7 | Particle reconstruction of volumetric particle image velocimetry with the strategy of machine learning显示文摘Three-dimensional particle reconstruction with limited two-dimensional projections is an under-determined inverse problem that the exact solution is often difficult to be obtained.In general,approximate solutions can be obtained by iterative optimization methods.In the current work,a practical particle reconstruction method based on a convolutional neural network(CNN)with geometry-informed features is proposed.The proposed technique can refine the particle reconstruction from a very coarse initial guess of particle distribution that is generated by any traditional algebraic reconstruction technique(ART)based methods.Compared with available ART-based algorithms,the novel technique makes significant improvements in terms of reconstruction quality,robustness to noise,and at least an order of magnitude faster in the offline stage. | Qi Gao Shaowu Pan Hongping Wang Runjie Wei Jinjun Wang | 2021 | Advances in Aerodynamics2021，3，1： | 0 |
| 8 | Correction: Particle reconstruction of volumetric particle image velocimetry with the strategy of machine learning显示文摘Following publication of the original article[1],the authors reported an error in the Funding number.The current Funding section is as below:This work was supported by the National Key R&D Program of China(No.2020YFA040070),the National Natural Science Foundation of China(grant No.11721202),the Program of State Key Laboratory of Marine Equipment(No.SKLMEA-K201910). | Qi Gao Shaowu Pan Hongping Wang Runjie Wei Jinjun Wang | 2022 | Advances in Aerodynamics2022，4，1： | 0 |
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