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	Optical Design of the NA1.1 Infinite Conjugate Microscopic Objective 会议论文 OAI收割 Changsha, PEOPLES R CHINA, 2022-11-11 作者: Guo Xinran; Chen Weilin; Chang Jun; Li Dongmei; Chen Qinfang \| 收藏 \| 浏览/下载：61/0 \| 提交时间：2023/10/19 Microscope objectives Optical design Genetic testing Mirror testing
	Design and Test of Flexible Supporting Structure for Ultra-light Mirror 期刊论文 OAI收割 Guangzi Xuebao/Acta Photonica Sinica, 2019, 卷号: 48, 期号: 12 作者: M.-Q.Shao; L.Zhang; L.Li; L.Wei; X.-Z.Jia \| 收藏 \| 浏览/下载：35/0 \| 提交时间：2020/08/24 Structural design,Constrained optimization,Flexible structures,Genetic algorithms,Ground supports,Mirrors,Optical testing,Optomechanics,Remote sensing,Vibrations (mechanical)
	A sensitive solid-phase time-resolved fluorescence immunoassay apparatus (EI CONFERENCE) 会议论文 OAI收割 International Symposium on Photoelectronic Detection and Imaging 2009: Laser Sensing and Imaging, June 17, 2009 - June 19, 2009, Beijing, China 作者: Song K.-F. 收藏 \| 浏览/下载：37/0 \| 提交时间：2013/03/25 In the device a He-Ne laser of flash frequency 1-20 Hz was adopted as exciting light source and three key technical problems have been solved successfully in order to enhance the detecting sensitivity and measuring stability of the device for time-resolved fluorimmunoassays(TRFIA) [1]. The first one is to design optimum exciting optical system so that the exciting light beam excite the sample most effectively. The second one is to have a project spectrum filter which can reduce the affection of the background light to the photomultiplier tube and also ensure influence of the stray light and mixed diffusion light to the sample fluorescence to the least the sample fluorescence through the integrating sphere and come to the grating monochromator The right wavelength will be chosed through changing the angle of incidence of the grating monochromator. The third one is to simulate the principle of sample averaging of BOXCAR averager. In the device SCM was used as primary controller and CPLD was used as timing controller. Through the preparation process signal-to-noise ratio(SNR) will be improved also adjust delay time ampling frequency and sampling number arbitrarily. By testing the sensitivity is 10-12mol/L(substance marked by Eu3+) examination repeat is &le2.5% examination linearity is from 10 -8mol/L to 10-12mol/L correlation coefficient is 99.98%(p&le0.01). The instrument is advanced for ultrasensitive detection of antigen and antibody and solve the tumor genetic variation the virus protein detection. 2009 SPIE.
	Study on color model conversion for camera with neural network based on the combination between second general revolving combination design and genetic algorithm (EI CONFERENCE) 会议论文 OAI收割 ICO20: Illumination, Radiation, and Color Technologies, August 21, 2005 - August 26, 2005, Changchun, China 作者: Li Z.; Zhou F.; Wang C.; Li Z. 收藏 \| 浏览/下载：36/0 \| 提交时间：2013/03/25 Munsell color system is selected to establish the mutual conversion between RGB and Lab* color model for camera. The color luminance meter and CCD camera synchronously measure the same color card XYZ value is gotten from the color luminance meter the training error is 0.000748566 it can show that the method combining second general revolving combination design with genetic algorithm can optimize the hidden-layer structure of neural network. Using the data of testing set to test this network and calculating the color difference between forecast value and true value the color picture captured from CCD camera is expressed for RGB value as the input of neural network and the Lab* value converted from XYZ value is regarded as the real color value of target card which the difference is not obvious comparing with forecast result the maximum is 5.6357 NBS namely the output of neural network. The neural network of two hidden-layers is considered the minimum is 0.5311 NBS so the second general revolving combination design is introduced into optimizing the structure of neural network and the average of color difference is 3.1744 NBS. which can carry optimization through unifying project design data processing and the precision of regression equation. Their mathematics model of encoding space is gained and the significance inspection shows the confidence degree of regression equation is 99%. The mathematics model is optimized by genetic algorithm optimization solution is gotten and function value of the goal is 0.0007168. The neural network of the optimization solution is trained