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Chinese Academy of Sciences Institutional Repositories Grid
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CAS IR Grid
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长春光学精密机械与物... [4]
地质与地球物理研究所 [1]
自动化研究所 [1]
沈阳自动化研究所 [1]
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OAI收割 [7]
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Stereo matching algorithm based on the combination of matching costs
会议论文
OAI收割
Hawaii, USA, July 31 - August 4, 2017
作者:
Wang ED(王恩德)
;
Zhu YL(朱亚龙)
;
Peng LY(彭良玉)
;
Yijun, Li
;
Tianyao, Wu
|
收藏
|
浏览/下载:14/0
|
提交时间:2018/10/08
Stereo Matching
Combination Of Matching Costs
Energy Function Optimization
A new strategy in drug design of chinese medicine: Theory, method and techniques
期刊论文
OAI收割
CHINESE JOURNAL OF INTEGRATIVE MEDICINE, 2012, 卷号: 18, 期号: 11, 页码: 803-806
作者:
Yang Hong-jun
;
Shen Dan
;
Xu Hai-yu
;
Lu Peng
收藏
|
浏览/下载:67/0
|
提交时间:2015/08/12
drug design of Chinese medicine
combination-activity relationship
prescription discovery
component identification
formula optimization
system modeling
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
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浏览/下载:22/0
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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
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浏览/下载:14/0
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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.
Using bidirectional binary particle swarm optimization for feature selection in feature-level fusion recognition system (EI CONFERENCE)
会议论文
OAI收割
2009 4th IEEE Conference on Industrial Electronics and Applications, ICIEA 2009, May 25, 2009 - May 27, 2009, Xi'an, China
作者:
Wang D.
;
Wang Y.
;
Wang Y.
;
Wang Y.
;
Wang Y.
收藏
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浏览/下载:25/0
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提交时间:2013/03/25
In feature-level fusion recognition system
the other is optimizing system sensor design to get outstanding cost performance. So feature selection become usually necessary to reduce dimensionality of the combination of multi-sensor features and improve system performance in system design. In general
there are two main missions. One is improving the recognition correct rate as soon as possible
the optimization is usually applied to feature selection because of its computational feasibility and validity. For further improving recognition accuracy and reducing selected feature dimensions
this paper presents a more rational and accurate optimization
Bidirectional Binary Particle Swarm Optimization (BBPSO) algorithm for feature selection in feature-level fusion target recognition system. In addition
we introduce a new evaluating function as criterion function in BBPSO feature selection method. At the last
we utilized Leave-One-Out method to validate the proposed method. The experiment results show that the proposed algorithm improves classification accuracy by two percentage points
while the selected feature dimensions are less one dimension than original Particle Swarm Optimization approach with 16 original feature dimensions. 2009 IEEE.
Common reflection surface stack for rugged surface topography
期刊论文
OAI收割
CHINESE JOURNAL OF GEOPHYSICS-CHINESE EDITION, 2006, 卷号: 49, 期号: 6, 页码: 1794-1801
作者:
Li Zhen-Chun
;
Sun Xiao-Dong
;
Liu Hong
|
收藏
|
浏览/下载:13/0
|
提交时间:2018/09/26
static correction
CRS stack
kinematic parameters
data redatuming
simulating annealing algorithm
combination optimization
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
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浏览/下载:36/0
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提交时间:2013/03/25
Munsell color system is selected to establish the mutual conversion between RGB and L*a*b* 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 L*a*b* 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
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