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浏览/检索结果: 共5条，第1-5条 帮助 限定条件 内容类型：会议论文     已选(0)清除 条数/页：5101520253035404550556065707580859095100   排序方式：请选择作者升序作者降序题名升序题名降序发表日期升序发表日期降序提交时间升序提交时间降序 A remaining useful life prediction approach for lithium-ion batteries using Kalman filter and an improved particle filter 会议论文 2016 IEEE INTERNATIONAL CONFERENCE ON PROGNOSTICS AND HEALTH MANAGEMENT (ICPHM), 2016-01-01 作者:  Mo, Baohua;  Yu, Jingsong;  Tang, Diyin;  Liu, Hao 收藏  |  浏览/下载：6/0  |  提交时间：2019/12/30 lithium-ion battery  remaining useful life  capacity prediction  Kalman filter  improved particle filter   A remaining useful life prediction approach for lithium-ion batteries using Kalman filter and an improved particle filter 会议论文 IEEE International Conference on Prognostics and Health Management (ICPHM), Carleton Univ, Ottawa, CANADA, 2016-06-20 作者:  Mo, Baohua;  Yu, Jingsong;  Tang, Diyin;  Liu, Hao 收藏  |  浏览/下载：3/0  |  提交时间：2019/12/30 lithium-ion battery  remaining useful life  capacity prediction  Kalman filter  improved particle filter   The new approach for infrared target tracking based on the particle filter algorithm (EI CONFERENCE) 会议论文 International Symposium on Photoelectronic Detection and Imaging 2011: Advances in Infrared Imaging and Applications, May 24, 2011 - May 24, 2011, Beijing, China Sun H.; Han H.-X. 收藏  |  浏览/下载：50/0  |  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).   Contour extracting with combination particle filtering and em algorithm (EI CONFERENCE) 会议论文 International Symposium on Photoelectronic Detection and Imaging, ISPDI 2007: Related Technologies and Applications, September 9, 2007 - September 12, 2007, Beijing, China Meng B.; Zhu M. 收藏  |  浏览/下载：19/0  |  提交时间：2013/03/25 The problem of extracting continuous structures from images is a difficult issue in early pattern recognition and image processings[1]. Tracking with contours in a filtering framework requires a dynamical model for prediction. Recently  Particle filter  is widely used because its multiple hypotheses and versatility within framework. However  the good choice of the propagation function is still its main problem. In this paper  an improved particle filter  EM-PF algorithm is proposed which using the EM (Expectation-Maximization) algorithm to learn the dynamical models. The EM algorithm can explicitly learn the parameters of the dynamical models from training sequences. The advantage of using the EM algorithm in particle filter is that it is capable of improve tracking contour by having accurate model parameters. Though the experiment results  we show how our EM-PF can be applied to produces more robust and accurate extracting.   Real time tracking by LOPF algorithm with mixture model (EI CONFERENCE) 会议论文 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：22/0  |  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.