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	A proteomic landscape of pharmacologic perturbations for functional relevance 期刊论文 OAI收割 JOURNAL OF PHARMACEUTICAL ANALYSIS, 2024, 卷号: 14, 期号: 1, 页码: 128-139 作者: Liu, Zhiwei; Jiang, Shangwen; Hao, Bingbing; Xie, Shuyu; Liu, Yingluo \| 收藏 \| 浏览/下载：27/0 \| 提交时间：2024/04/09 Proteomics Drug Perturbation Drug target Drug combination
	Oligosaccharides from fucosylated glycosaminoglycan prevent breast cancer metastasis in mice by inhibiting heparanase activity and angiogenesis 期刊论文 OAI收割 PHARMACOLOGICAL RESEARCH, 2021, 卷号: 166, 页码: 105527 作者: Zhou,Lutan; Yin,Ronghua; Gao,Na; Sun,Huifang; Chen,Dingyuan \| 收藏 \| 浏览/下载：59/0 \| 提交时间：2022/04/02 Oligosaccharides Heparanase Angiogenesis Metastasis FIBROBLAST-GROWTH-FACTOR TUMOR-GROWTH SULFATE PROTEOGLYCANS MAMMALIAN HEPARANASE COMBINATION MECHANISMS DISCOVERY BINDING TARGET PI-88
	Experimental investigation on coherent beam combination of a three-element fiber array based on target-in-the-loop technique 期刊论文 OAI收割 ACTA PHYSICA SINICA, 2012, 卷号: 61, 期号: 3 作者: Geng Chao; Li Xin-Yang; Zhang Xiao-Jun; Rao Chang-Hui 收藏 \| 浏览/下载：32/0 \| 提交时间：2015/07/10 fiber array coherent beam combination target-in-the-loop adaptive fiber optics collimator
	Reentry guidance based on feedback linearization (EI CONFERENCE) 会议论文 OAI收割 2011 International Conference on Electronics, Communications and Control, ICECC 2011, September 9, 2011 - September 11, 2011, Ningbo, China Li D.-W. 收藏 \| 浏览/下载：31/0 \| 提交时间：2013/03/25 This paper presents a new reentry guidance algorithm for RLV (Reusable Launching Vehicle). The algorithm consists of two integrated components: trajectory planning algorithm and tracking algorithm. The most striking feature of algorithm here lies in that both planning and tracking are executed directly in height-velocity space which is different from the methodology of configuration of drag in traditional shuttle guidance. In the session of trajectory planning all trajectory constraints can be expressed with upper bound and lower bound in height-velocity space then a linear interpolation is carried to search the nominal trajectory satisfying the requirement of downrange and target constraints. Then the tracking algorithm uses feedback linearization method to track this nominal profile and meet all constraints. Another typical feature of this algorithm is the strategy of downrange extension using FPA (flight path angle) controller to fulfill the requirement of large downrange. Proper combination of planning-tracking algorithm and FPA controller can bring great flexibility and adaptability to reentry guidance. The algorithm is proved to be robust enough to accommodate the model error and noises in the dynamics. 2011 IEEE.
	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 \| 浏览/下载：30/0 \| 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.
	Pautomatic Sea Target Detection Based on Wavelet Transform 期刊论文 OAI收割 Journal of China Ordnance, 2009, 期号: 1, 页码: 36-40 作者: Pei LL(裴立力); Luo HB(罗海波) 收藏 \| 浏览/下载：35/0 \| 提交时间：2012/05/29 information processing target detection wavelet analysis mutual energy combination maximum energy determination
	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：39/0 \| 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
	Potential energy surfaces for nucleon exchanging in dinuclear systems 期刊论文 OAI收割 HIGH ENERGY PHYSICS AND NUCLEAR PHYSICS-CHINESE EDITION, 2003, 卷号: 27, 期号: 146, 页码: 48-52 作者: Zhao, EG; Li, JF; Xu, HS; Li, WF; Zuo, W \| 收藏 \| 浏览/下载：15/0 \| 提交时间：2010/10/29 Super-heavy Nucleus Driven Potential Optimum Proj Ectile Target Combination Optimum Excitation Energy