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您的检索式：作者名="Ye Cuiling"
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| 1 | Numerical simulation of the pulsing air separation field based on CFD显示文摘The flow field of pulsing air separation is normally in an unsteady turbulence state.With the application of the basic principles of multiphase turbulent flows,we established the dynamical computational model,which shows a remarkable variation of the unstable pulsing air flow field.CFD(computational fluid dynamics) was used to conduct the numerical simulation of the actual geometric model of the classifier.The inside velocity of the flowing fields was analyzed later.The simulation results indicate that the designed structure of the active pulsing air classifier provided a favorable environment for the separation of the particles with different physical characters by density.We shot the movement behaviors of the typical tracer grains in the active pulsing flow field using a high speed dynamic camera.The displacement and velocity curves of the particles in the continuous impulse periods were then analyzed.The experimental results indicate that the effective separation by density of the particles with the same settling velocity and different ranges of the density and particle size can be achieved in the active pulsing airflow field.The experimental results provide an agreement with the simulation results. | He Jingfeng He Yaqun Zhao Yuemin Duan Chenlong Ye Cuiling | 2012 | International Journal of Mining Science and Technology2012，22，2： | 12 |
| 2 | P-selectin-mediated platelet adhe sion promotes the metastasis of murine melanoma cclls显示文摘 | Qi Cuiling Wei Bo Ye Jie | 2014 | PLoS One2014，9，91： | 1 |
| 3 | Graph-Based Dimensionality Reduction for Hyperspectral Imagery: A Review显示文摘Hyperspectral image(HSI)contains a wealth of spectral information,which makes fine classification of ground objects possible.In the meanwhile,overly redundant information in HSI brings many challenges.Specifically,the lack of training samples and the high computational cost are the inevitable obstacles in the design of classifier.In order to solve these problems,dimensionality reduction is usually adopted.Recently,graph-based dimensionality reduction has become a hot topic.In this paper,the graph-based methods for HSI dimensionality reduction are summarized from the following aspects.1)The traditional graph-based methods employ Euclidean distance to explore the local information of samples in spectral feature space.2)The dimensionality-reduction methods based on sparse or collaborative representation regard the sparse or collaborative coefficients as graph weights to effectively reduce reconstruction errors and represent most important information of HSI in the dictionary.3)Improved methods based on sparse or collaborative graph have made great progress by considering global low-rank information,local intra-class information and spatial information.In order to compare typical techniques,three real HSI datasets were used to carry out relevant experiments,and then the experimental results were analysed and discussed.Finally,the future development of this research field is prospected. | Zhen Ye Shihao Shi Zhan Cao Lin Bai Cuiling Li Tao Sun Yongqiang Xi | 2021 | Journal of Beijing Institute of Technology2021，30，2： | 1 |
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