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	Continuance intention to use the mobile interest-based community: An integrated theoretical model and empirical study 期刊论文 OAI收割 chinese journal of library and information science, 2015, 卷号: 8, 期号: 2, 页码: 52-68 作者: HU Jiming; YU Jing 收藏 \| 浏览/下载：69/0 \| 提交时间：2015/08/18 Mobile interest-based community Continuance intention Expectation confirmation model (ECM) Experiential value
	Effect of Zn doping on the hydrogenolysis of glycerol over ZnNiAl catalyst 期刊论文 OAI收割 journal of molecular catalysis a-chemical, 2014, 卷号: 395, 页码: 1-6 Li, Xiaoru; Zhang, Chao; Cheng, Haiyang; He,Limin; Lin,Weiwei; Yu,Yancun; Zhao,Fengyu 收藏 \| 浏览/下载：95/0 \| 提交时间：2015/10/20 Nickel based catalyst is of interest in the industrial catalytic processes and it is always modified by doping a second element to improve its catalytic properties. Understanding the role of dopant is extremely helpful in tailoring the active centers. In this work Zn could induce a significant improvement of the catalytic performance of NiAl for the hydrogenolysis of glycerol. The Zn doped ZnNiAl catalysts exhibited high activity and the improved selectivity to 1 2-propanediol it was about 2 times higher than the original ones. The ZnNi alloy formed in ZnNiAl was responsible for the noticeable catalytic performance. They preferred to coordinate with the end hydroxyl group of glycerol promoted the cleavage of C-O bond in glycerol and resulted in the dominant formation of 1 2-propanediol. The findings described here will provide a useful knowledge for rational design of nickel-based catalysts as well as reveal a new reaction model for the hydrogenolysis of glycerol. (C) 2014 Elsevier B.V. All rights reserved.
	Features extraction and matching of teeth image based on the SIFT algorithm (EI CONFERENCE) 会议论文 OAI收割 2012 2nd International Conference on Computer Application and System Modeling, ICCASM 2012, July 27, 2012 - July 29, 2012, Shenyang, China 作者: Wang X.; Wang X.; Wang X. 收藏 \| 浏览/下载：44/0 \| 提交时间：2013/03/25 Using of SIFT algorithm in the image of teeth model can detect the features of the teeth image effectively. In this approach first search over all scales and image locations by using a difference-of-Gaussian function to identify potential interest points that are invariant to scale and orientation. Second select keypoints based on measures of their stability and a detailed model is fit to determine location and scale at each candidate location. Third assign one or more orientations to each keypoint location based on local image gradient directions. Last measure the local image gradients at the selected scale in the region around each keypoint. And then use the KNN algorithm to match the features. Through lots of experiments and comparing with other feature extraction methods this method can detect the features of the teeth model effectively and offer some available parameters for 3D reconstruction of the teeth model. the authors.
	综合集成研讨厅中的专家兴趣建模及应用学位论文 OAI收割工学硕士, 中国科学院自动化研究所: 中国科学院研究生院, 2011 刘凯收藏 \| 浏览/下载：71/0 \| 提交时间：2015/09/02 综合集成研讨厅体系网页正文抽取兴趣模型非负矩阵分解个性化推荐 Cyberspace for Workshop of Metasynthetic Engineering(CWME) Webpage Body Text Extraction Interest-Based Model Non-negative Matrix Factorization(NMF) Personalized Recommendation
	Real-time motive vehicle detection with adaptive background updating model and HSV colour space (EI CONFERENCE) 会议论文 OAI收割 4th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optical Test and Measurement Technology and Equipment, November 19, 2008 - November 21, 2008, Chengdu, China Rong-Hui Z.; Bai Y.; Hong-guang J.; Chen T. 收藏 \| 浏览/下载：83/0 \| 提交时间：2013/03/25 In the transportation monitor system we set up the area of interest (AOI) of the vehicle model and adjust the size of AOI dynamically in order to track vehicle accurately. The results of experiment show that motive vehicle detection by adopting digital image is one of key technologies. To detect motive vehicle accurately the arithmetic proposed in the paper can suppress shadow availably we establish an adaptive background updating model firstly. Noise is suppressed by using modality filter detect motive vehicle accurately and satisfy real-time motive vehicle tracking. 2009 SPIE. and we obtain binary image by using maximum entropy to choose dynamic adaptive threshold. Based on positive information of shadow and aspect feature of motive vehicle we adopt HSV colour space and double threshold to solve the problem of vehicle shadow. According to prediction result of Kalman filtering