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	Prediction of Potentially Suitable Distributions of Codonopsis pilosula in China Based on an Optimized MaxEnt Model 期刊论文 OAI收割 FRONTIERS IN ECOLOGY AND EVOLUTION, 2021, 卷号: 9, 页码: 17 作者: Yan, Huyong; He, Jiao; Xu, Xiaochuan; Yao, Xinyu; Wang, Guoyin \| 收藏 \| 浏览/下载：39/0 \| 提交时间：2022/08/22 optimized MaxEnt Codonopsis pilosula regularization multiplier feature combination potential distribution multivariate environmental similarity surface analysis
	A Multifunctional Combination Incubator 期刊论文 OAI收割 ENERGIES, 2020, 卷号: 13, 期号: 24, 页码: 22 作者: Li SY(李少英); Qu ZQ(屈中权); Song ZM(宋智明) \| 收藏 \| 浏览/下载：51/0 \| 提交时间：2021/03/01 combination incubator adjustable light window debugging platform temperature fluctuations optimized incubator SLITA
	Determination of optimum process parameters in gas-assisted injection molding (EI CONFERENCE) 会议论文 OAI收割 2011 International Conference on Electrical Information and Mechatronics, ICEIM2011, December 23, 2011 - December 25, 2011, Jiaozuo, China Zi K.; Chen L. 收藏 \| 浏览/下载：20/0 \| 提交时间：2013/03/25 With finite element analysis software Moldflow numerical simulation and studies about FM truck roof handle were conducted on gas-assisted injection molding process. The influences of melt pre-injection shot gas pressure delay time and melt temperature were observed by using multi-factor orthogonal experimental method. According to the analysis of the factors' impact on evaluation index the optimized parameter combination is obtained. Therefore the optimization design of technological parameters is done. The results show that during the gas-assisted injection molding optimum pre-injection shot is 94% gas pressure is 15MPa delay time is 0.5s melt temperature is 240 C. This study provided a more practical approach for the gas-assisted injection molding process optimization.
	Optimum design of the carbon fiber thin-walled baffle for the space-based camera (EI CONFERENCE) 会议论文 OAI收割 International Symposium on Photoelectronic Detection and Imaging 2011: Space Exploration Technologies and Applications, May 24, 2011 - May 26, 2011, Beijing, China Yan Y.; Gu S.; An Y.; Jin G. 收藏 \| 浏览/下载：14/0 \| 提交时间：2013/03/25 The thin-walled baffle design of the space-based camera is an important job in the lightweight space camera research task for its stringent quality requirement and harsh mechanical environment especially for the thin-walled baffle of the carbon fiber design. In the paper an especially thin-walled baffle of the carbon fiber design process was described and it is sound significant during the other thin-walled baffle design of the space camera. The designer obtained the design margin of the thin-walled baffle that structural stiffness and strength can tolerated belong to its development requirements through the appropriate use of the finite element analysis of the walled parameters influence sensitivity to its structural stiffness and strength. And the designer can determine the better optimization criterion of thin-walled baffle during the geometric parameter optimization process in such guiding principle. It sounds significant during the optimum design of the thin-walled baffle of the space camera. For structural stiffness and strength of the carbon fibers structure which can been designed the effect of the optimization will be more remarkable though the optional design of the parameters chose. Combination of manufacture process and design requirements the paper completed the thin-walled baffle structure scheme selection and optimized the specific carbon fiber fabrication technology though the FEM optimization and the processing cost and process cycle are retrenchment/saved effectively in the method. Meanwhile the weight of the thin-walled baffle reduced significantly in meet the design requirements under the premise of the structure. The engineering prediction had been adopted and the related result shows that the thin-walled baffle satisfied the space-based camera engineering practical needs very well its quality reduced about 20% the final assessment index of the thin-walled baffle were superior to the overall design requirements significantly. The design method is reasonable and efficient to the other thin-walled baffle that mass and work environment requirement is requirement harsh. 2011 SPIE.
	A fine resampling algorithm for general particle filters 会议论文 OAI收割 4th international congress on image and signal processing, cisp 2011, shanghai, china, october 15, 2011 - october 17, 2011 CaoBei; MaCaiWen; LiuZhenTao 收藏 \| 浏览/下载：21/0 \| 提交时间：2012/07/09 particle filter fine resampling particle degeneracy sample impoverishment optimized combination
	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