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	A C1q Domain Containing Protein from Scallop Chlamys farreri Serving as Pattern Recognition Receptor with Heat-Aggregated IgG Binding Activity 期刊论文 OAI收割 PLOS ONE, 2012, 卷号: 7, 期号: 8, 页码: e43289 作者: Wang, Leilei; Wang, Lingling; Zhang, Huan; Zhou, Zhi; Siva, Vinu S. 收藏 \| 浏览/下载：35/0 \| 提交时间：2013/09/24 C-TYPE LECTIN CLASSICAL COMPLEMENT PATHWAY OYSTER CRASSOSTREA-GIGAS IMMUNE GENE REPERTOIRE GLOBULAR HEAD B-CHAIN C1Q-DOMAIN-CONTAINING PROTEIN INNATE IMMUNITY HUMAN-MONOCYTES A-CHAINS
	A MLP-PNN neural network for CCD image super-resolution in wavelet packet domain (EI CONFERENCE) 会议论文 OAI收割 2008 International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM 2008, October 12, 2008 - October 14, 2008, Dalian, China Zhao X.; Fu D.; Zhai L. 收藏 \| 浏览/下载：68/0 \| 提交时间：2013/03/25 Image super-resolution methods process an input image sequence of a scene to obtain a still image with increased resolution. Classical approaches to this problem involve complex iterative minimization procedures typically with high computational costs. In this paper is proposed a novel algorithm for super-resolution that enables a substantial decrease in computer load. First decompose and reconstruct the image by wavelet packet. Before constructing the image use neural network in place of other rebuilding method to reconstruct the coefficients in the wavelet packet domain. Second probabilistic neural network architecture is used to perform a scattered-point interpolation of the image sequence data in the wavelet packet domain. The network kernel function is optimally determined for this problem by a MLP-PNN (Multi Layer Perceptron - Probabilistic Neural Network) trained on synthetic data. Network parameters dependent on the sequence noise level. This super-sampled image is spatially Altered to correct finite pixel size effects to yield the final high-resolution estimate. This method can decrease the calculation cost and get perfect PSNR. Results are presented showing the quality of the proposed method. 2008 IEEE.
	An improved adaptive neural network method for control system (EI CONFERENCE) 会议论文 OAI收割 2006 International Conference on Machine Learning and Cybernetics, August 13, 2006 - August 16, 2006, Dalian, China Wang L.-M.; Xie M.-J.; Wu D.-Y. 收藏 \| 浏览/下载：18/0 \| 提交时间：2013/03/25 Classical methods for designing a controller depend on the accuracy of system model. However plant's models and other parts in a physical system can not accurately represent all possible dynamics. Thus the controller designed is usually not the optimal one. In this article a new simple adaptive control method which combines the classical frequency domain method with the neural network theory is proposed. Firstly we can obtain a controller using classical method. Secondly we use the coefficients in digitized controller equation as the initial values of an Adaline network. Finally LMS learning rules is used to adjust the weights adaptively. Experimental results show that this method is very effective in improving the performance of conventional controller. 2006 IEEE.
	The Schwarzian derivative in several complex variables (II) 期刊论文 OAI收割 CHINESE ANNALS OF MATHEMATICS SERIES B, 1998, 卷号: 19, 期号: 1, 页码: 1-8 作者: Gong, S; Yu, QH; Zheng, X \| 收藏 \| 浏览/下载：8/0 \| 提交时间：2018/07/30 Schwarzian derivative classical domain linear fractional mapping