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	A comparative study of methods for delineating sphere of urban influence: A case study on central China 期刊论文 OAI收割 CHINESE GEOGRAPHICAL SCIENCE, 2014, 卷号: 24, 期号: 6, 页码: 751-762 作者: Wang Hao; Deng Yu; Tian Enze; Wang Kaiyong \| 收藏 \| 浏览/下载：27/0 \| 提交时间：2019/09/26 Gravity Model Improved Field Model Sphere Of Urban Influence Regional Planning Central China
	A comparative study of methods for delineating sphere of urban influence: A case study on central China 期刊论文 OAI收割 CHINESE GEOGRAPHICAL SCIENCE, 2014, 卷号: 24, 期号: 6, 页码: 751-762 作者: Wang Hao; Deng Yu; Tian Enze; Wang Kaiyong \| 收藏 \| 浏览/下载：13/0 \| 提交时间：2019/09/26 Gravity Model Improved Field Model Sphere Of Urban Influence Regional Planning Central China
	A comparative study of methods for delineating sphere of urban influence: A case study on central China 期刊论文 OAI收割 CHINESE GEOGRAPHICAL SCIENCE, 2014, 卷号: 24, 期号: 6, 页码: 751-762 作者: Wang Hao; Deng Yu; Tian Enze; Wang Kaiyong \| 收藏 \| 浏览/下载：12/0 \| 提交时间：2019/09/26 Gravity Model Improved Field Model Sphere Of Urban Influence Regional Planning Central China
	Double inverted pendulum control based on three-loop PID and improved BP neural network (EI CONFERENCE) 会议论文 OAI收割 2011 2nd International Conference on Digital Manufacturing and Automation, ICDMA 2011, August 5, 2011 - August 7, 2011, Zhangjiajie, Hunan, China 作者: Fan Y. 收藏 \| 浏览/下载：35/0 \| 提交时间：2013/03/25 To deal with the defects of BP neural networks used in balance control of inverted pendulum such as longer train time and converging in partial minimum this article reaLizes the control of double inverted pendulum with improved BP algorithm of artificial neural networks(ANN) builds up a training model of test simulation and the BP network is 6-10-1 structure. Tansig function is used in hidden layer and PureLin function is used in output layer LM is used in training algorithm. The training data is acquried by three-loop PID algorithm. The model is learned and trained with Matlab calculating software and the simuLink simulation experiment results prove that improved BP algorithm for inverted pendulum control has higher precision better astringency and lower calculation. This algorithm has wide appLication on nonLinear control and robust control field in particular. 2011 IEEE.
	The new approach for infrared target tracking based on the particle filter algorithm (EI CONFERENCE) 会议论文 OAI收割 International Symposium on Photoelectronic Detection and Imaging 2011: Advances in Infrared Imaging and Applications, May 24, 2011 - May 24, 2011, Beijing, China 作者: Sun H.; Han H.-X.; Sun H. 收藏 \| 浏览/下载：57/0 \| 提交时间：2013/03/25 Target tracking on the complex background in the infrared image sequence is hot research field. It provides the important basis in some fields such as video monitoring precision and video compression human-computer interaction. As a typical algorithms in the target tracking framework based on filtering and data connection the particle filter with non-parameter estimation characteristic have ability to deal with nonlinear and non-Gaussian problems so it were widely used. There are various forms of density in the particle filter algorithm to make it valid when target occlusion occurred or recover tracking back from failure in track procedure but in order to capture the change of the state space it need a certain amount of particles to ensure samples is enough and this number will increase in accompany with dimension and increase exponentially this led to the increased amount of calculation is presented. In this paper particle filter algorithm and the Mean shift will be combined. Aiming at deficiencies of the classic mean shift Tracking algorithm easily trapped into local minima and Unable to get global optimal under the complex background. From these two perspectives that "adaptive multiple information fusion" and "with particle filter framework combining" we expand the classic Mean Shift tracking framework.Based on the previous perspective we proposed an improved Mean Shift infrared target tracking algorithm based on multiple information fusion. In the analysis of the infrared characteristics of target basis Algorithm firstly extracted target gray and edge character and Proposed to guide the above two characteristics by the moving of the target information thus we can get new sports guide grayscale characteristics and motion guide border feature. Then proposes a new adaptive fusion mechanism used these two new information adaptive to integrate into the Mean Shift tracking framework. Finally we designed a kind of automatic target model updating strategy to further improve tracking performance. Experimental results show that this algorithm can compensate shortcoming of the particle filter has too much computation and can effectively overcome the fault that mean shift is easy to fall into local extreme value instead of global maximum value.Last because of the gray and fusion target motion information this approach also inhibit interference from the background ultimately improve the stability and the real-time of the target track. 2011 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).
	Real time tracking by LOPF algorithm with mixture model (EI CONFERENCE) 会议论文 OAI收割 MIPPR 2007: Automatic Target Recognition and Image Analysis; and Multispectral Image Acquisition, November 15, 2007 - November 17, 2007, Wuhan, China Meng B.; Zhu M.; Han G.; Wu Z. 收藏 \| 浏览/下载：26/0 \| 提交时间：2013/03/25 A new particle filter-the Local Optimum Particle Filter (LOPF) algorithm is presented for tracking object accurately and steadily in visual sequences in real time which is a challenge task in computer vision field. In order to using the particles efficiently we first use Sobel algorithm to extract the profile of the object. Then we employ a new Local Optimum algorithm to auto-initialize some certain number of particles from these edge points as centre of the particles. The main advantage we do this in stead of selecting particles randomly in conventional particle filter is that we can pay more attentions on these more important optimum candidates and reduce the unnecessary calculation on those negligible ones in addition we can overcome the conventional degeneracy phenomenon in a way and decrease the computational costs. Otherwise the threshold is a key factor that affecting the results very much. So here we adapt an adaptive threshold choosing method to get the optimal Sobel result. The dissimilarities between the target model and the target candidates are expressed by a metric derived from the Bhattacharyya coefficient. Here we use both the counter cue to select the particles and the color cur to describe the targets as the mixture target model. The effectiveness of our scheme is demonstrated by real visual tracking experiments. Results from simulations and experiments with real video data show the improved performance of the proposed algorithm when compared with that of the standard particle filter. The superior performance is evident when the target encountering the occlusion in real video where the standard particle filter usually fails.