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Imaging theory of nonlinear second harmonic and third harmonic generations in confocal microscopy

science china(Physics,Mechanics & Astronomy) 2004年第1期47卷 8-16页

作者： tang zhilie1, XING Da2.& LIU Songhao2.1. Department of Physics,{2., guangzhou 51.631. china 2. school for information and optoelectronic science and engineering,{2., guangzhou 51.631. china correspondence {12. be addressed to tang zhilie (email: tangzhl@***) received april 2., 2003 1. Department of Physics South China Normal University 510631 Guangzhou China 2. school for information and optoelectronic science and engineering South China Normal University 510631 Guangzhou China

The imaging theory of nonlinear second harmonic generation (SHG) and third harmonic generation (THG) in confocal microscopy is presented in this paper. The nonlinear effect of SHG and THG on the imaging properties of confocal microscopy has been analyzed in detail by the imaging theory. It is proved that the imaging process of SHG and THG in confocal microscopy, which is different from conventional coherent imaging or incoherent imaging, can be divided into two different processes of coherent imaging. The three-dimensional point spread functions (3D-PSF) of SHG and THG confocal microscopy are derived based on the nonlinear principles of SHG and THG. The imaging properties of SHG and THG confocal microscopy are discussed in detail according to its 3D-PSF. It is shown that the resolution of SHG and THG confocal microscopy is higher than that of single- and two-photon confocal microscopy.

关键词： second harmonic generation, third harmonic generation, confocal microscopy, three-dimensional point spread function.

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A novel index to evaluate discretization methods: A case study of flood susceptibility assessment based on random forest

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Geoscience Frontiers 2021年第6期12卷 313-325页

作者： Xianzhe tang Takashi Machimura Wei Liu Jiufeng Li Haoyuan Hong Graduate school of engineering Osaka UniversityYamadaoka 2-1SuitaOsaka 565-0871Japan school of Geography South China Normal UniversityGuangzhou 510631China Jiangsu Provincial Key Laboratory of Geographic information science and Technology International Institute for Earth System ScienceNanjing UniversityNanjing 210023JiangsuChina Department of Geography and Regional Research University of ViennaVienna 1010Austria

The selection of a suitable discretization method(DM) to discretize spatially continuous variables(SCVs)is critical in ML-based natural hazard susceptibility assessment. However, few studies start to consider the influence due to the selected DMs and how to efficiently select a suitable DM for each SCV. These issues were well addressed in this study. The information loss rate(ILR), an index based on the information entropy, seems can be used to select optimal DM for each SCV. However, the ILR fails to show the actual influence of discretization because such index only considers the total amount of information of the discretized variables departing from the original SCV. Facing this issue, we propose an index, information change rate(ICR), that focuses on the changed amount of information due to the discretization based on each cell, enabling the identification of the optimal DM. We develop a case study with Random Forest(training/testing ratio of 7 : 3) to assess flood susceptibility in Wanan County, *** area under the curve-based and susceptibility maps-based approaches were presented to compare the ILR and ICR. The results show the ICR-based optimal DMs are more rational than the ILR-based ones in both cases. Moreover, we observed the ILR values are unnaturally small(<1%), whereas the ICR values are obviously more in line with general recognition(usually 10%–30%). The above results all demonstrate the superiority of the ICR. We consider this study fills up the existing research gaps, improving the MLbased natural hazard susceptibility assessments.

关键词： Machine learning Natural hazards information change rate Discretization method

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